非监督的结构学习及其应用
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摘要
在机器学习领域中,数据的表示方式是其中的核心问题。传统的方法经常通过特征向量的方式将数据表示为高维空间中的点。特征向量的表示方式由于简单直观的特性得到广泛的研究。但是,近年来的一些研究表明,单一的特征向量表示很难描述数据的某些特性。因此,基于结构的数据表示方式已经成为研究人员关注的重点。本文的研究重点是通过非监督的结构方式学习数据的结构。由于数据结构空间的搜索是组合问题,会出现组合爆炸现象,因此如何通过近似途径快速地搜索数据的结构空间是非监督学习的重点。根据不同任务的特性,我们提出了不同非监督学习算法。在文本聚类任务中,我们提出了层次谱聚类算法来进行文本的层次聚类和语义树的生成。在图像的物体识别任务中,我们提出了结构推导算法和知识传播策略学习物体的图模型的结构和参数。
论文主要研究内容与创新成果如下:
1.我们提出基于概率文法和马尔可夫场的物体模型(Probabilistic Grammar-MarkovModel,PGMM)。PGMM模型的学习过程只需要极少量的监督信息,即PGMM模型的结构和参数都可以通过非监督的方式进行学习。关键点三元组被提出作为PGMM模型的基本组成单位。结构推导算法通过对关键点三元组的组合来生成复杂的模型结构。由于PGMM模型的结构采用了联合树的形式,允许动态规划算法的使用,因此PGMM模型可以快速推理和参数学习。实验结果证明,PGMM模型能处理在未知背景中的物体识别和定位。在学习和推理过程中,PGMM模型允许物体在2D范围内的任意变化(位置、旋转和尺寸)。由于概率文法模型的帮助,PGMM模型不但能够处理物体具有不同的形态,还能够处理由不同的物体类别构成的混合类数据。
2.我们提出一种学习概率物体模型(Probabilistic Object Model,POM)的新方法。POM模型综合各种视觉特征,能够同时执行图像分类、图像分割和图像物体识别等多个视觉任务的能力。我们通过组合使用互补图像特征的基本的概率物体模型的方式来学习POM模型的结构。在模型的学习过程中,我们提出了知识传播策略。该策略允许一个基本概率物体模型为其它基本概率物体模型提供信息,并且指导它们的学习过程。知识传播策略显著地降低了训练过程对数据的要求,也提高了推理过程的速度。PGMM模型是POM模型中的一个组成部分。相对于PGMM模型,POM模型不仅保留了PGMM模型的所有优点,而且能够执行更多的视觉任务。同时,在图像分类任务中,POM模型也具有更高的性能。
3.我们提出一种新颖的层次聚类算法,谱层次聚类算法(Spectral Hierarchi-calClustering,SHC)。SHC算法是基于谱图理论的层次聚类算法。它采用AMG(Algebraic Multi-Grid)数值计算方法,通过迭代地权重融合方式,自底向上地分层合并节点进行聚类。AMG数值计算方法的应用保证了算法能够得到近似全局最优解。实验证明了SHC算法在文本聚类算法中的性能。SHC算法最终得到的自然并且不规则的聚类结构也是其一大特性。基于博客标签的语义树生成实验证明了SHC算法的聚类结构的合理性。它使得用户浏览语义树更为方便自然。
综上所述,本文提出新颖的非监督学习模型结构的算法,将它们应用于物体识别和文本聚类任务中,并通过实验证明它们的合理性和有效性。
The representation of data is one of the key in machine learning.Traditional methods usually represent the data as points in high dimensional space via feature vector. Due to its simplicity,great progress and developments have been made in methods based on feature vector.However,recent research showed that feature vector can not describe some properties of data.Therefore,the representation based on structure attracts more and more attention of researchers.This dissertation focuses on the unsupervised structure learning.Because of the huge space of data structure,how to rapidly search the space by approximate method is the key problem of unsupervised structure learning.According to properties of the tasks,different algorithms are proposed. For document clustering,spectral hierarchical clustering algorithm is proposed to do hierarchical document clustering and blog tag taxonomy construction.For object recognition,structure induction and knowledge propagation are used to learn the structure and parameters of the graphical model of object.
The main content and innovations in this dissertation are as follows:
1.We introduce a Probabilistic Grammar-Markov Model(PGMM) which couples probabilistic context free grammars and Markov Random Fields.PGMM is a generative model defined over attributed features and is used to detect and classify objects in natural images.PGMM is designed so that it can perform rapid inference,parameter learning,and the more difficult task of structure induction. PGMM can deal with unknown 2D pose(position,orientation and scale) in both inference and learning different aspects of the model.The PGMM can be learnt in an unsupervised manner where the image can contain one objects of different object categories or even be pure background.We first study the weakly supervised case,where each image contains an example of the object category,and then generalize to less supervised cases.The experiments on a subset of the Caltech dataset show that our results are comparable with the current state of the art and our inference is performed in less than five seconds.
2.We present a method to learn probabilistic object models(POMs) with minimal supervision which can exploit different visual cues and perform tasks such as classification,segmentation,and recognition.We formulate this as a structure induction and learning task and our strategy is to learn and combine basic POMs that make use of complementary image cues.We describe a novel structure induction procedure which uses knowledge propagation to enable one POM to provide information to other POM and "teach them"(which greatly reduces the amount of supervision required for training and speeds up the inference). We give detailed experimental analysis on large datasets which show that the POMs is invariant to scale and rotation of the object(for learning and inference) and performs inference rapidly.In addition,the experimental results show that POMs can be applied to learn hybrid objects classes(i.e.when there are several objects and the identity of the object in each image is unknown).We emphasize that these models can match between different objects from the same category and hence enable object recognition.
3.We present spectral hierarchical clustering(SHC),a novel hierarchical clustering algorithm.Spectral analysis on SHC is provided by spectral graph theory which is commonly used in flat clustering but novel for hierarchical clustering.SHC uses the numeric techniques of Algebraic Multi-Grid method to perform fine-tocoarse weighted aggregation recursively.We evaluate the proposed algorithm on a number of different benchmark datasets.The comparison results show that our algorithm performs much better than the state-of-the-art hierarchical clustering algorithms.SHC is applied to the application of blog tag taxonomy construction. The results demonstrate that SHC performs more consistently with human judgments than other methods.Moreover,the resulting natural irregular tag hierarchy obtained by SHC is easier for users to browse the structure and locate the tags of interest.
论文主要研究内容与创新成果如下:
1.我们提出基于概率文法和马尔可夫场的物体模型(Probabilistic Grammar-MarkovModel,PGMM)。PGMM模型的学习过程只需要极少量的监督信息,即PGMM模型的结构和参数都可以通过非监督的方式进行学习。关键点三元组被提出作为PGMM模型的基本组成单位。结构推导算法通过对关键点三元组的组合来生成复杂的模型结构。由于PGMM模型的结构采用了联合树的形式,允许动态规划算法的使用,因此PGMM模型可以快速推理和参数学习。实验结果证明,PGMM模型能处理在未知背景中的物体识别和定位。在学习和推理过程中,PGMM模型允许物体在2D范围内的任意变化(位置、旋转和尺寸)。由于概率文法模型的帮助,PGMM模型不但能够处理物体具有不同的形态,还能够处理由不同的物体类别构成的混合类数据。
2.我们提出一种学习概率物体模型(Probabilistic Object Model,POM)的新方法。POM模型综合各种视觉特征,能够同时执行图像分类、图像分割和图像物体识别等多个视觉任务的能力。我们通过组合使用互补图像特征的基本的概率物体模型的方式来学习POM模型的结构。在模型的学习过程中,我们提出了知识传播策略。该策略允许一个基本概率物体模型为其它基本概率物体模型提供信息,并且指导它们的学习过程。知识传播策略显著地降低了训练过程对数据的要求,也提高了推理过程的速度。PGMM模型是POM模型中的一个组成部分。相对于PGMM模型,POM模型不仅保留了PGMM模型的所有优点,而且能够执行更多的视觉任务。同时,在图像分类任务中,POM模型也具有更高的性能。
3.我们提出一种新颖的层次聚类算法,谱层次聚类算法(Spectral Hierarchi-calClustering,SHC)。SHC算法是基于谱图理论的层次聚类算法。它采用AMG(Algebraic Multi-Grid)数值计算方法,通过迭代地权重融合方式,自底向上地分层合并节点进行聚类。AMG数值计算方法的应用保证了算法能够得到近似全局最优解。实验证明了SHC算法在文本聚类算法中的性能。SHC算法最终得到的自然并且不规则的聚类结构也是其一大特性。基于博客标签的语义树生成实验证明了SHC算法的聚类结构的合理性。它使得用户浏览语义树更为方便自然。
综上所述,本文提出新颖的非监督学习模型结构的算法,将它们应用于物体识别和文本聚类任务中,并通过实验证明它们的合理性和有效性。
The representation of data is one of the key in machine learning.Traditional methods usually represent the data as points in high dimensional space via feature vector. Due to its simplicity,great progress and developments have been made in methods based on feature vector.However,recent research showed that feature vector can not describe some properties of data.Therefore,the representation based on structure attracts more and more attention of researchers.This dissertation focuses on the unsupervised structure learning.Because of the huge space of data structure,how to rapidly search the space by approximate method is the key problem of unsupervised structure learning.According to properties of the tasks,different algorithms are proposed. For document clustering,spectral hierarchical clustering algorithm is proposed to do hierarchical document clustering and blog tag taxonomy construction.For object recognition,structure induction and knowledge propagation are used to learn the structure and parameters of the graphical model of object.
The main content and innovations in this dissertation are as follows:
1.We introduce a Probabilistic Grammar-Markov Model(PGMM) which couples probabilistic context free grammars and Markov Random Fields.PGMM is a generative model defined over attributed features and is used to detect and classify objects in natural images.PGMM is designed so that it can perform rapid inference,parameter learning,and the more difficult task of structure induction. PGMM can deal with unknown 2D pose(position,orientation and scale) in both inference and learning different aspects of the model.The PGMM can be learnt in an unsupervised manner where the image can contain one objects of different object categories or even be pure background.We first study the weakly supervised case,where each image contains an example of the object category,and then generalize to less supervised cases.The experiments on a subset of the Caltech dataset show that our results are comparable with the current state of the art and our inference is performed in less than five seconds.
2.We present a method to learn probabilistic object models(POMs) with minimal supervision which can exploit different visual cues and perform tasks such as classification,segmentation,and recognition.We formulate this as a structure induction and learning task and our strategy is to learn and combine basic POMs that make use of complementary image cues.We describe a novel structure induction procedure which uses knowledge propagation to enable one POM to provide information to other POM and "teach them"(which greatly reduces the amount of supervision required for training and speeds up the inference). We give detailed experimental analysis on large datasets which show that the POMs is invariant to scale and rotation of the object(for learning and inference) and performs inference rapidly.In addition,the experimental results show that POMs can be applied to learn hybrid objects classes(i.e.when there are several objects and the identity of the object in each image is unknown).We emphasize that these models can match between different objects from the same category and hence enable object recognition.
3.We present spectral hierarchical clustering(SHC),a novel hierarchical clustering algorithm.Spectral analysis on SHC is provided by spectral graph theory which is commonly used in flat clustering but novel for hierarchical clustering.SHC uses the numeric techniques of Algebraic Multi-Grid method to perform fine-tocoarse weighted aggregation recursively.We evaluate the proposed algorithm on a number of different benchmark datasets.The comparison results show that our algorithm performs much better than the state-of-the-art hierarchical clustering algorithms.SHC is applied to the application of blog tag taxonomy construction. The results demonstrate that SHC performs more consistently with human judgments than other methods.Moreover,the resulting natural irregular tag hierarchy obtained by SHC is easier for users to browse the structure and locate the tags of interest.
引文
[1]I.Biederman,An Invitation to cognitive science:Visual Object Recognition.MIT Press,1995.
[2]Y.Lecun,L.Bottou,Y.Bengio,and P.Haffner,"Gradient-based learning applied to document recognition," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1998.
[3]Y.LeCun,F.J.Huang,and L.Bottou,"Learning methods for generic object recognition with invariance to pose and lighting," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2004.
[4]S.Belongie,J.Malik,and J.Puzicha,"Shape matching and object recognition using shape contexts," IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.24,no.4.
[5]M.Turk and A.Pentland,"Eigenfaces for recognition," Journal of Cognitive Neuroscience,vol.3,1991.
[6]T.Poggio and K.K.Sung,"Finding human faces with a gaussian mixture distribution-based face model," in Proceedings of Asian Conference on Computer Vision,1995.
[7]K.K.Sung,“Learning and example selection for object and pattern detection,” PhD Thesis,1996.
[8]K.K.Sung and T.Poggio,“Example-based learning for view-based human face detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.20,1998.
[9]H.A.Rowley,S.Baluja,and T.Kanade,“Rotation invariant neural network-based face detection,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1998.
[10]P.Juell and R.A.Marsh,“A hierarchical neural network for human face detection,” Pattern Recognition,vol.29,1996.
[11]E.Osuna,R.Freund,and F.Girosi,“Training support vector machines:an application to face detection,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1997,pp.130-136.
[12]B.Heisele,T.Poggio,and M.Pontil,“Face detection in still gray images.”
[13]P.A.Viola and M.J.Jones,“Rapid object detection using a boosted cascade of simple features,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2001.
[14]S.Z.Li,Z.Zhang,H.-Y.Shum,and H.Zhang,“Floatboost learning for classification,” in Advances in Neural Information Processing Systems,2002.
[15]S.Z.Li and Z.Zhang,“Floatboost learning and statistical face detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.26,2004.
[16]R.Lienhart and J.Maydt,“An extended set of haar-like features for rapid object detection,” in Proceedings of IEEE International Conference on Image Processing,2002.
[17]C.Huang,H.Ai,Y.Li,and S.Lao,“Learning sparse features in granular space for multi-view face detection,” in Proceedings of International Conference on Automatic Face and Gesture Recognition,2006.
[18]T.Mita,T.Kaneko,and O.Hori,“Joint haar-like features for face detection,” in Proceedings of IEEE International Conference on Computer Vision,2005.
[19]B.Froba and A.Ernst,“Face detection with the modified census transform,” in Proceedings of International Conference on Automatic Face and Gesture Recognition,2004.
[20]C.Huang,H.Ai,Y Li,and S.Lao,“Vector boosting for rotation invariant multi-view face detection,” in Proceedings of IEEE International Conference on Computer Vision,2005.
[21]R.Xiao,L.Zhu,and H.Zhang,“Boosting chain learning for object detection,” in Proceedings of IEEE International Conference on Computer Vision,2003.
[22]C.Huang,H.Ai,Y.Li,and S.Lao,“High-performance rotation invariant multiview face detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.29,2007.
[23]M.A.Fischler and R.A.Elschlager,“The representation and matching of pictorial structures,” IEEE Transactions on Computers,vol.22,no.1,pp.67-92,1973.
[24]C.Papageorgiou,M.Oren,and T.Poggio,“A general framework for object detection,”in Proceedings of IEEE International Conference on Computer Vision,1998.
[25]N.Dalai and B.Triggs,“Histograms of oriented gradients for human detection,”in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005.
[26]M.M.Fleck,D.A.Forsyth,and C.Bregler,“Finding naked people,” in Proceedings of European Conference on Computer Vision,1996.
[27]D.A.Forsyth and M.M.Fleck,“Body plans,”in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1997.
[28]K.Mikolajczyk,C.Schmid,and A.Zisserman,“Human detection based on a probabilistic assembly of robust part detectors,”in Proceedings of European Conference on Computer Vision,2004.
[29]P.F.Felzenszwalb and D.P.Huttenlocher,“Pictorial structures for object recognition,” International Journal of Computer Vision,vol.61,no.1,pp.55-79,2005.
[30]——,“Efficient matching of pictorial structures,”in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2000.
[31]J.Huang,R.Kumar,M.Mitra,W.-J.Zhu,and R.Zabih,“Image indexing using color correlograms,”in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1997,pp.762-768.
[32]G.Csurka,C.Dance,J.Willamowski,L.Fan,C.Bray,and G.Csurka,“Visual categorization with bags of keypoints,”in Proceedings of European Conference on Computer Vision International Workshop on Statistical Learning in Computer Vision,2004.
[33]G.Dorko and C.Schmid,“Object class recognition using discriminative local features,” Technical Report.
[34]A.Opelt,M.Fussenegger,A.Pinz,and P.Auer,“Weak hypotheses and boosting for generic object detection and recognition,” in Proceedings of European Conference on Computer Vi- sion,2004.
[35]M.C.Burl and P.Perona,“Recognition of planar object classes,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1996.
[36]M.C.Burl,M.Weber,and P.Perona,“A probabilistic approach to object recognition using local photometry and global geometry,” in Proceedings of European Conference on Computer Vision,1998.
[37]M.Weber,“Unsupervised learning of models for object recognition,” PhD Thesis,2000.
[38]R.Fergus,P.Perona,and A.Zisserman,“Object class recognition by unsupervised scaleinvariant learning,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2003,pp.264-271.
[39]T.K.Leung and J.Malik,“Contour continuity in region based image segmentation,” in Proceedings of European Conference on Computer Vision,1998.
[40]T.K.Leung,M.C.Burl,and P.Perona,“Finding faces in cluttered scenes using labeled random graph matching,” in Proceedings of IEEE International Conference on Computer Vision,1995.
[41]M.Weber,W Einhauser,M.Welling,and P.Perona,“Viewpoint-invariant learning and detection of human heads,” in Proceedings of International Conference on Automatic Face and Gesture Recognition,2000.
[42]M.Weber,M.Welling,and P.Perona,“Towards automatic discovery of object categories,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2000.
[43]——,“Unsupervised learning of models for recognition,” in Proceedings of European Conference on Computer Vision,2000.
[44]R.Fergus,P.Perona,and A.Zisserman,“A sparse object category model for efficient learning and exhaustive recognition,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005,pp.380-387.
[45]L.Fei-Fei,R.Fergus,and P.Perona,“Learning generative visual models from few training examples:An incremental bayesian approach tested on 101 object categories,” Proceedings of Conference on Computer Vision and Image Understanding,vol.106,pp.59-70,2007.
[46]F.-F.Li,R.Fergus,and P.Perona,“One-shot learning of object categories,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.28,no.4,pp.594-611,2006.
[47]D.J.Crandall,P.F.Felzenszwalb,and D.P.Huttenlocher,“Spatial priors for part-based recog- nition using statistical models,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005,pp.10-17.
[48]D.J.Crandall and D.P.Huttenlocher,“Weakly supervised learning of part-based spatial models for visual object recognition,” in Proceedings of European Conference on Computer Vision,2006,pp.16-29.
[49]E.Borenstein and S.Ullman,“Class-specific top-down segmentation,” in Proceedings of European Conference on Computer Vision,2002,pp.109-124.
[50]A.B.Torralba,K.P.Murphy,and W.T.Freeman,“Sharing features:Efficient boosting procedures for multiclass object detection,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2004.
[51]A.C.Berg,T.L.Berg,and J.Malik,“Shape matching and object recognition using low distortion correspondences,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005.
[52]J.M.Winn and N.Jojic,“Locus:Learning object classes with unsupervised segmentation,” in Proceedings of IEEE International Conference on Computer Vision,2005,pp.756-763.
[53]K.Kummamuru,R.Lotlikar,S.Roy,K.Singal,and R.Krishnapuram,“A hierarchical monothetic document clustering algorithm for summarization and browsing search results,” in Proceedings of International Conference on World Wide Web,2004,pp.658-665.
[54]D.J.Lawrie and W.B.Croft,“Generating hierarchical summaries for web searches,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2003,pp.457-458.
[55]C.H.Brooks and N.Montanez,“Improved annotation of the blogosphere via autotagging and hierarchical clustering,” in Proceedings of International Conference on World Wide Web,2006.
[56]S.-L.Chuang and L.-F.Chien,“Towards automatic generation of query taxonomy:A hierarchical query clustering approach,” in Proceedings of IEEE International Conference on Data Mining,2002.
[57]P.Cimiano,A.Hotho,and S.Staab,“Comparing conceptual,divisive and agglomerative clustering for learning taxonomies from text,” in Proceedings of Eureopean Conference on Artificial Intelligence,2004,pp.435-439.
[58]P.Ferragina and A.Gulli,“The anatomy of a hierarchical clustering engine for web-page,news and book snippets,” in Proceedings of IEEE International Conference on Data Mining, 2004,pp.395-398.
[59]——,“A personalized search engine based on web-snippet hierarchical clustering,”in Proceedings of International Conference on World Wide Web,2005,pp.801-810.
[60]A.Griffiths,L.A.Robinson,and P.Willett,“Hierarchical agglomerative clustering methods for automatic document classification,” Journal of Documentation,vol.3,1984.
[61]A.Brandt,S.McCormick,and J.Ruge,“Algebraic multigrid (amg)for automatic multigrid solutions with application to geodetic computations,”Fort Coolins,CO,Tech.Rep.,1982.
[62]P.Berkhin,“Survey of clustering data mining techniques,” Accrue Software,Tech.Rep.,2002.
[63]R.Xu and D.W.II,“Survey of clustering algorithms,”IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.16.
[64]Y.Zhao and G.Karypis,“Evaluation of hierarchical clustering algorithms for document datasets,” in Proceedings of ACM Conference on Information and Knowledge Management,2002,pp.515-524.
[65]S.Guha,R.Rastogi,and K.Shim,“Cure:An efficient clustering algorithm for large databases,” Information System,vol.26,2001.
[66]——,“Rock:A robust clustering algorithm for categorical attributes,”Information System,vol.25,2000.
[67]T.Zhang,R.Ramakrishnan,and M.Livny,“Birch:A new data clustering algorithm and its applications,” Data Mining and Knowledge Discovery,vol.1,1997.
[68]G.Karypis,E.-H.Han,and V.Kumar,“Chameleon:Hierarchical clustering using dynamic modeling,” IEEE Computer,vol.32,1999.
[69]D.R.Cutting,D.R.Karger,J.O.Pedersen,and J.W.Tukey,“Scatter/gather:a cluster-based approach to browsing large document collections,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,1992,pp.318-329.
[70]B.Larsen and C.Aone,“Fast and effective text mining using linear-time document clustering,” in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,1999,pp.16-22.
[71]M.Steinbach,G.Karypis,and V.Kumar,“A comparison of document clustering techniques,” in Proceedings of ACM SIGKDD Workshop on Text Mining,2000.
[72]M.Belkin and P.Niyogi,“Laplacian eigenmaps for dimensionality reduction and data repre- sentation,” Neural Computation,vol.15,2003.
[73]A.Y Ng,M.I.Jordan,and Y.Weiss,“On spectral clustering:Analysis and an algorithm,” in Advances in Neural Information Processing Systems,2001.
[74]P.K.Chan,M.D.F.Schlag,and J.Y.Zien,“Spectral k-way ratio-cut partitioning and clustering,” in Proceedings of Design Automation Conference,1993.
[75]J.Shi and J.Malik,“Normalized cuts and image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.22,no.8,pp.888-905,2000.
[76]C.H.Q.Ding,X.He,H.Zha,M.Gu,and H.D.Simon,“A min-max cut algorithm for graph partitioning and data clustering,” in Proceedings of IEEE International Conference on Data Mining,2001.
[77]F.Wang,C.Zhang,and T.Li,“Regularized clustering for documents,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2007.
[78]X.He,D.Cai,H.Liu,and W.-Y Ma,“Locality preserving indexing for document representation,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2004,pp.96-103.
[79]D.Cai,X.He,and J.Han,“Document clustering using locality preserving indexing,” IEEE Transactions on Knowledge and Data Engineering,vol.17,2005.
[80]W.Xu,X.Liu,and Y.Gong,“Document clustering based on non-negative matrix factorization,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2003,pp.267-273.
[81]C.Ding,T.Li,W.Peng,and H.Park,“Document clustering using locality preserving indexing,” Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2006.
[82]T.Li and C.H.Q.Ding,“The relationships among various nonnegative matrix factorization methods for clustering,” in Proceedings of IEEE International Conference on Data Mining,2006.
[83]T.Li,S.Ma,and M.Ogihara,“Document clustering via adaptive subspace iteration,”in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2004,pp.218-225.
[84]W.Xu and Y Gong,“Document clustering by concept factorization,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2004.
[85]F.Beil,M.Ester,and X.Xu,“Frequent term-based text clustering,” in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2002,pp.436-442.
[86]B.C.M.Fung,K.Wang,and M.Ester,“Hierarchical document clustering using frequent itemsets,” in Proceedings of the Third SIAM International Conference on Data Mining,2003.
[87]I.S.Dhillon,“Co-clustering documents and words using bipartite spectral graph partitioning,” in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2001.
[1]D.A.McAllester,M.Collins,and F.Pereira,"Case-factor diagrams for structured probabilistic modeling," in Proceedings of Conference on Uncertainty in Artificial Intelligence,2004,pp.382-391.
[2]J.D.Lafferty,A.McCallum,and F.C.N.Pereira,"Conditional random fields:Probabilistic models for segmenting and labeling sequence data," in Proceedings of International Conference on Machine Learning,2001,pp.282-289.
[3]D.Klein and C.D.Manning,"Natural language grammar induction using a constituent-context model," in Advances in Neural Information Processing Systems,2001,pp.35-42.
[4]R.Dechter and R.Mateescu,“And/or search spaces for graphical models,” Artificial Intelligence,vol.171,no.2-3,pp.73-106,2007.
[5]H.Chen,Z.Xu,Z.Liu,and S.C.Zhu,“Composite templates for cloth modeling and sketching,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2006.
[6]L.S.Zettlemoyer and M.Collins,“Learning to map sentences to logical form:Structured classification with probabilistic categorial grammars,” in Proceedings of Conference on Uncertainty in Artificial Intelligence,2005,pp.658-666.
[7]B.D.Ripley,Pattern Recognition and Neural Networks.New York,NY,USA:Cambridge University Press,1996.
[8]C.Manning and H.Schuetze,Foundations of statistical natural language processing.Cambridge,Mass,USA:MIT Press,1999.
[9]Z.Tu,X.Chen,A.L.Yuille,and S.C.Zhu,“Image parsing:Unifying segmentation,detection,and recognition,” in Proceedings of IEEE International Conference on Computer Vision,2003,pp.18-25.
[10]R.Fergus,P.Perona,and A.Zisserman,“Object class recognition by unsupervised scaleinvariant learning,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2003,pp.264-271.
[11]H.Barlow,“Unsupervised learning,” Neural Computation,vol.1,pp.295-311,1989.
[12]L.Fei-Fei,R.Fergus,and P.Perona,“Learning generative visual models from few training examples:An incremental bayesian approach tested on 101 object categories,” Proceedings of Conference on Computer Vision and Image Understanding,vol.106,pp.59-70,2007.
[13]R.Fergus,P.Perona,and A.Zisserman,“A sparse object category model for efficient learning and exhaustive recognition,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005,pp.380-387.
[14]D.J.Crandall and D.P.Huttenlocher,“Weakly supervised learning of part-based spatial models for visual object recognition,” in Proceedings of European Conference on Computer Vision,2006,pp.16-29.
[15]D.J.Crandall,P.F.Felzenszwalb,and D.P.Huttenlocher,“Spatial priors for part-based recognition using statistical models,”in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005,pp.10-17.
[16]G.Csurka,C.Dance,J.Willamowski,L.Fan,C.Bray,and G.Csurka,“Visual categoriza- tion with bags of keypoints,” in Proceedings of European Conference on Computer Vision International Workshop on Statistical Learning in Computer Vision,2004.
[17]M.Meila and M.I.Jordan,“Learning with mixtures of trees,” Journal of Machine Learning Research,vol.1,pp.1-48,2000.
[18]S.D.Pietra,V.J.D.Pietra,and J.D.Lafferty,“Inducing features of random fields,” IEEE Transactions on Pattern Analysis andMachine Intelligence,vol.19,no.4,pp.380-393,1997.
[19]S.C.Zhu,Y.N.Wu,and D.Mumford,“Minimax entropy principle and its application to texture modeling,” Neural Computation,vol.9,no.8,pp.1627-1660,1997.
[20]A.McCallum,“Efficiently inducing features of conditional random fields,” in Conference on Uncertainty in Artificial Intelligence,2003,pp.403-410.
[21]N.Friedman,“The bayesian structural em algorithm,”in Proceedings of Conference on Uncertainty in Artificial Intelligence,1998,pp.129-138.
[22]L.Shams and C.von der Malsburg,“Are object shape primitives learnable?”Neurocomputing,vol.26-27,pp.855-863,1999.
[23]J.Ponce,T.L.Berg,M.Everingham,D.A.Forsyth,M.Hebert,S.Lazebnik,M.Marszalek,C.Schmid,B.C.Russell,A.Torralba,C.K.I.Williams,J.Zhang,and A.Zisserman,“Dataset issues in object recognition,”in Toward Category-Level Object Recognition,2006,pp.29-48.
[24]F.V.Jensen,S.L.Lauritzen,and K.G.Olesen,“Bayesian updating in causal probabilistic networks by local computations,”Computational Statistics Quaterly,vol.4,pp.269-282,1990.
[25]T.Kadir and M.Brady,“Saliency,scale and image description,”International Journal of Computer Vision,vol.45,no.2,pp.83-105,2001.
[26]D.G.Lowe,“Distinctive image features from scale-invariant keypoints,”International Journal of Computer Vision,vol.60,no.2,pp.91-110,2004.
[27]S.Lazebnik,C.Schmid,and J.Ponce,“Semi-local affine parts for object recognition,” in Proceedings of IEEE International Workshop on Biologically Motivated Computer Vision,2004.
[28]Y.Amit and D.Geman,“A computational model for visual selection,”Neural Computation,vol.11,no.7,pp.1691-1715,1999.
[29]Y.Jin and S.Geman,“Context and hierarchy in a probabilistic image model,”in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2006,pp.2145-2152.
[30]J.M.Coughlan,A.L.Yuille,C.English,and D.Snow,“Efficient deformable template detection and localization without user initialization,” Proceedings of Conference on Computer Vision and Image Understanding,pp.303-319,2000.
[31]R.M.Neal and G.E.Hinton,“A view of the em algorithm that justifies incremental,sparse,and other variants,”Learning in graphical models,pp.355-368,1999.
[32]L.Zhu,Y.Chen,and A.L.Yuille,“Unsupervised learning of a probabilistic grammar for object detection and parsing,”in Advances in Neural Information Processing Systems,2006,pp.1617-1624.
[33]——,“Unsupervised learning of probabilistic grammar-markov models for object categories,” IEEE Transactions on Pattern Analysis and Machine Intelligence,2008.
[34]Y.Chen,L.Zhu,C.Lin,A.L.Yuille,and H.Zhang,“Rapid inference on a novel and/or graph for object detection,segmentation and parsing,”in Advances in Neural Information Processing Systems,2007.
[1]L.Zhu,Y.Chen,and A.L.Yuille,"Unsupervised learning of a probabilistic grammar for object detection and parsing," in Advances in Neural Information Processing Systems,2006, pp.1617-1624.
[2]L.Zhu,Y.Chen,and A,"Unsupervised learning of probabilistic grammar-markov models for object categories,"IEEE Transactions on Pattern Analysis and Machine Intelligence,2008.
[3]R.Fergus,P.Perona,and A.Zisserman,"Object class recognition by unsupervised scaleinvariant learning," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2003,pp.264-271.
[4]R.Fergus,P,"A sparse object category model for efficient learning and exhaustive recognition," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005,pp.380-387.
[5]D.J.Crandall and D.P.Huttenlocher,"Weakly supervised learning of part-based spatial models for visual object recognition," in Proceedings of European Conference on Computer Vision,2006,pp.16-29.
[6]B.Leibe,A.Leonardis,and B.Schiele,"Combined object categorization and segmentation with an implicit shape model,”in Proceedings of European Conference on Computer Vision'04 Workshop on Statistical Learning in Computer Vision,2004,pp.17-32.
[7]E.Borenstein and S.Ullman,“Learning to segment,” in Proceedings of European Conference on Computer Vision,2004,pp.315-328.
[8]A.Levin and Y.Weiss,“Learning to combine bottom-up and top-down segmentation,”in Proceedings of European Conference on Computer Vision,2006,pp.581-594.
[9]X.Ren,C.Fowlkes,and J.Malik,“Cue integration for figure/ground labeling,” in Advances in Neural Information Processing Systems,2005.
[10]J.M.Winn and N.Jojic,“Locus:Learning object classes with unsupervised segmentation,” in Proceedings of IEEE International Conference on Computer Vision,2005,pp.756-763.
[11]J.Sivic,B.C.Russell,A.A.Efros,A.Zisserman,and W.Freeman,“Discovering object categories in image collections,”in Proceedings of IEEE International Conference on Computer Vision,2005,pp.370-377.
[12]L.Cao and L.Fei-Fei,“Spatially coherent latent topic model for concurrent object segmentation and classification,” in Proceedings of IEEE International Conference on Computer Vision,2007.
[13]Y.Boykov and V.Kolmogorov,“An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,”in International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition,2001,pp.359-374.
[14]A.Blake,C.Rother,M.Brown,P.Perez,and P.H.S.Torr,“Interactive image segmentation using an adaptive gmmrf model,”in Proceedings of European Conference on Computer Vision,2004,pp.428-441.
[15]Y Boykov and M.-P.Jolly,“Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images,” in Proceedings of IEEE International Conference on Computer Vision,2001,pp.105-112.
[16]C.Rother,V.Kolmogorov,and A.Blake,“”grabcut”:interactive foreground extraction using iterated graph cuts,”ACM Transactions on Graphics,vol.23,no.3,pp.309-314,2004.
[17]T.Kadir and M.Brady,“Saliency,scale and image description,” International Journal of Computer Vision,vol.45,no.2,pp.83-105,2001.
[18]D.G.Lowe,“Distinctive image features from scale-invariant keypoints,”International Journal of Computer Vision,vol.60,no.2,pp.91-110,2004.
[19]M.P.Kumar,P.H.S.Torr,and A.Zisserman,“Obj cut,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005,pp.18-25.
[20]L.Fei-Fei,R.Fergus,and P.Perona,“Learning generative visual models from few training examples:An incremental bayesian approach tested on 101 object categories,”Proceedings of Conference on Computer Vision and Image Understanding,vol.106,pp.59-70,2007.
[1]S.-L.Chuang and L.-F.Chien,"Towards automatic generation of query taxonomy:A hierarchical query clustering approach," in Proceedings of IEEE International Conference on Data Mining,2002.
[2]P.Cimiano,A.Hotho,and S.Staab,"Comparing conceptual,divisive and agglomerative clustering for learning taxonomies from text," in Proceedings of Eureopean Conference on Artificial Intelligence,2004,pp.435-439.
[3]P.Clerkin,P.Cunningham,and C.Hayes,"Ontology discovery for the semantic web using hierarchical clustering," in Proceedings of European Conference on Machine Learning Workshop on Semantic Web Mining,2001.
[4]D.R.Cutting,D.R.Karger,J.O.Pedersen,and J.W.Tukey,"Scatter/gather:a cluster-based approach to browsing large document collections,”in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,1992,pp.318-329.
[5]K.Kummamuru,R.Lotlikar,S.Roy,K.Singal,and R.Krishnapuram,“A hierarchical monothetic document clustering algorithm for summarization and browsing search results,”in Proceedings of International Conference on World Wide Web,2004,pp.658-665.
[6]D.J.Lawrie and W.B.Croft,“Generating hierarchical summaries for web searches,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2003,pp.457-458.
[7]C.H.Brooks and N.Montanez,“Improved annotation of the blogosphere via autotagging and hierarchical clustering,” in Proceedings of International Conference on World Wide Web,2006.
[8]P.Ferragina and A.Gulli,“The anatomy of a hierarchical clustering engine for web-page,news and book snippets,” in Proceedings of IEEE International Conference on Data Mining,2004,pp.395-398.
[9]——,“A personalized search engine based on web-snippet hierarchical clustering,” in Proceedings of International Conference on World Wide Web,2005,pp.801-810.
[10]Y.Zhao and G.Karypis,“Evaluation of hierarchical clustering algorithms for document datasets,” in Proceedings of ACM Conference on Information and Knowledge Management,2002,pp.515-524.
[11]A.Brandt,S.McCormick,and J.Ruge,“Algebraic multigrid (amg)for automatic multigrid solutions with application to geodetic computations,” Fort Coolins,CO,Tech.Rep.,1982.
[12]B.Larsen and C.Aone,“Fast and effective text mining using linear-time document clustering,” in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,1999,pp.16-22.
[13]M.Steinbach,G.Karypis,and V.Kumar,“A comparison of document clustering techniques,” in Proceedings of ACM SIGKDD Workshop on Text Mining,2000.
[14]P.K.Chan,M.D.F.Schlag,and J.Y.Zien,“Spectral k-way ratio-cut partitioning and clustering,” in Proceedings of Design Automation Conference,1993.
[15]J.Shi and J.Malik,“Normalized cuts and image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.22,no.8,pp.888-905,2000.
[16]C.H.Q.Ding,X.He,H.Zha,M.Gu,and H.D.Simon,“A min-max cut algorithm for graph partitioning and data clustering,” in Proceedings of IEEE International Conference on Data Mining,2001.
[17]Y.Koren,L.Carmel,and D.Harel,“Ace:A fast multiscale eigenvectors computation for drawing huge graphs,” in Proceedings of IEEE Symposium on Information Visualization,2002,p.137.
[18]E.Sharon,M.Galun,D.Sharon,R.Basri,and A.Brandt,“Hierarchy and adaptivity in segmenting visual scenes,” Nature,pp.810-813,2006.
[19]E.Sharon,A.Brandt,and R.Basri,“Fast multiscale image segmentation,”in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2000,pp.1070-1077.
[20]X.He,D.Cai,H.Liu,and W.-Y.Ma,“Locality preserving indexing for document representation,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2004,pp.96-103.
[21]P.Pantel and D.Lin,“Document clustering with committees,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2002,pp.199-206.
[22]W.Xu,X.Liu,and Y.Gong,“Document clustering based on non-negative matrix factorization,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2003,pp.267-273.
[23]B.Mandhani,S.Joshi,and K.Kummamuru,“A matrix density based algorithm to hierarchically co-cluster documents and words,” in Proceedings of International Conference on World Wide Web,2003,pp.511-518.
[24]T.Li,S.Ma,and M.Ogihara,“Document clustering via adaptive subspace iteration,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2004,pp.218-225.
[25]N.Ueda and K.Saito,“Parametric mixture models for multi-labeled text,”in Advances in Neural Information Processing Systems,2003.
[26]R.Baeza-Yates and B.Ribeiro-Nero,Modern Information Retrieval.New York:Addison-Wesley,1999.
[27]Y.Huang and T.M.Mitchell,“Text clustering with extended user feedback,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2006,pp.413-420.
[28]C.Leacock,Combining local context and wordnet similarity for word sense identification.Cambridge:MIT Press,1998.
[29]A.P.Bradley,“Use of the area under the roc curve in the evaluation of machine learning algorithms,”Pattern Recognition,vol.30,pp.1145-1159,1997.
[2]Y.Lecun,L.Bottou,Y.Bengio,and P.Haffner,"Gradient-based learning applied to document recognition," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1998.
[3]Y.LeCun,F.J.Huang,and L.Bottou,"Learning methods for generic object recognition with invariance to pose and lighting," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2004.
[4]S.Belongie,J.Malik,and J.Puzicha,"Shape matching and object recognition using shape contexts," IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.24,no.4.
[5]M.Turk and A.Pentland,"Eigenfaces for recognition," Journal of Cognitive Neuroscience,vol.3,1991.
[6]T.Poggio and K.K.Sung,"Finding human faces with a gaussian mixture distribution-based face model," in Proceedings of Asian Conference on Computer Vision,1995.
[7]K.K.Sung,“Learning and example selection for object and pattern detection,” PhD Thesis,1996.
[8]K.K.Sung and T.Poggio,“Example-based learning for view-based human face detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.20,1998.
[9]H.A.Rowley,S.Baluja,and T.Kanade,“Rotation invariant neural network-based face detection,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1998.
[10]P.Juell and R.A.Marsh,“A hierarchical neural network for human face detection,” Pattern Recognition,vol.29,1996.
[11]E.Osuna,R.Freund,and F.Girosi,“Training support vector machines:an application to face detection,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1997,pp.130-136.
[12]B.Heisele,T.Poggio,and M.Pontil,“Face detection in still gray images.”
[13]P.A.Viola and M.J.Jones,“Rapid object detection using a boosted cascade of simple features,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2001.
[14]S.Z.Li,Z.Zhang,H.-Y.Shum,and H.Zhang,“Floatboost learning for classification,” in Advances in Neural Information Processing Systems,2002.
[15]S.Z.Li and Z.Zhang,“Floatboost learning and statistical face detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.26,2004.
[16]R.Lienhart and J.Maydt,“An extended set of haar-like features for rapid object detection,” in Proceedings of IEEE International Conference on Image Processing,2002.
[17]C.Huang,H.Ai,Y.Li,and S.Lao,“Learning sparse features in granular space for multi-view face detection,” in Proceedings of International Conference on Automatic Face and Gesture Recognition,2006.
[18]T.Mita,T.Kaneko,and O.Hori,“Joint haar-like features for face detection,” in Proceedings of IEEE International Conference on Computer Vision,2005.
[19]B.Froba and A.Ernst,“Face detection with the modified census transform,” in Proceedings of International Conference on Automatic Face and Gesture Recognition,2004.
[20]C.Huang,H.Ai,Y Li,and S.Lao,“Vector boosting for rotation invariant multi-view face detection,” in Proceedings of IEEE International Conference on Computer Vision,2005.
[21]R.Xiao,L.Zhu,and H.Zhang,“Boosting chain learning for object detection,” in Proceedings of IEEE International Conference on Computer Vision,2003.
[22]C.Huang,H.Ai,Y.Li,and S.Lao,“High-performance rotation invariant multiview face detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.29,2007.
[23]M.A.Fischler and R.A.Elschlager,“The representation and matching of pictorial structures,” IEEE Transactions on Computers,vol.22,no.1,pp.67-92,1973.
[24]C.Papageorgiou,M.Oren,and T.Poggio,“A general framework for object detection,”in Proceedings of IEEE International Conference on Computer Vision,1998.
[25]N.Dalai and B.Triggs,“Histograms of oriented gradients for human detection,”in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005.
[26]M.M.Fleck,D.A.Forsyth,and C.Bregler,“Finding naked people,” in Proceedings of European Conference on Computer Vision,1996.
[27]D.A.Forsyth and M.M.Fleck,“Body plans,”in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1997.
[28]K.Mikolajczyk,C.Schmid,and A.Zisserman,“Human detection based on a probabilistic assembly of robust part detectors,”in Proceedings of European Conference on Computer Vision,2004.
[29]P.F.Felzenszwalb and D.P.Huttenlocher,“Pictorial structures for object recognition,” International Journal of Computer Vision,vol.61,no.1,pp.55-79,2005.
[30]——,“Efficient matching of pictorial structures,”in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2000.
[31]J.Huang,R.Kumar,M.Mitra,W.-J.Zhu,and R.Zabih,“Image indexing using color correlograms,”in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1997,pp.762-768.
[32]G.Csurka,C.Dance,J.Willamowski,L.Fan,C.Bray,and G.Csurka,“Visual categorization with bags of keypoints,”in Proceedings of European Conference on Computer Vision International Workshop on Statistical Learning in Computer Vision,2004.
[33]G.Dorko and C.Schmid,“Object class recognition using discriminative local features,” Technical Report.
[34]A.Opelt,M.Fussenegger,A.Pinz,and P.Auer,“Weak hypotheses and boosting for generic object detection and recognition,” in Proceedings of European Conference on Computer Vi- sion,2004.
[35]M.C.Burl and P.Perona,“Recognition of planar object classes,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1996.
[36]M.C.Burl,M.Weber,and P.Perona,“A probabilistic approach to object recognition using local photometry and global geometry,” in Proceedings of European Conference on Computer Vision,1998.
[37]M.Weber,“Unsupervised learning of models for object recognition,” PhD Thesis,2000.
[38]R.Fergus,P.Perona,and A.Zisserman,“Object class recognition by unsupervised scaleinvariant learning,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2003,pp.264-271.
[39]T.K.Leung and J.Malik,“Contour continuity in region based image segmentation,” in Proceedings of European Conference on Computer Vision,1998.
[40]T.K.Leung,M.C.Burl,and P.Perona,“Finding faces in cluttered scenes using labeled random graph matching,” in Proceedings of IEEE International Conference on Computer Vision,1995.
[41]M.Weber,W Einhauser,M.Welling,and P.Perona,“Viewpoint-invariant learning and detection of human heads,” in Proceedings of International Conference on Automatic Face and Gesture Recognition,2000.
[42]M.Weber,M.Welling,and P.Perona,“Towards automatic discovery of object categories,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2000.
[43]——,“Unsupervised learning of models for recognition,” in Proceedings of European Conference on Computer Vision,2000.
[44]R.Fergus,P.Perona,and A.Zisserman,“A sparse object category model for efficient learning and exhaustive recognition,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005,pp.380-387.
[45]L.Fei-Fei,R.Fergus,and P.Perona,“Learning generative visual models from few training examples:An incremental bayesian approach tested on 101 object categories,” Proceedings of Conference on Computer Vision and Image Understanding,vol.106,pp.59-70,2007.
[46]F.-F.Li,R.Fergus,and P.Perona,“One-shot learning of object categories,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.28,no.4,pp.594-611,2006.
[47]D.J.Crandall,P.F.Felzenszwalb,and D.P.Huttenlocher,“Spatial priors for part-based recog- nition using statistical models,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005,pp.10-17.
[48]D.J.Crandall and D.P.Huttenlocher,“Weakly supervised learning of part-based spatial models for visual object recognition,” in Proceedings of European Conference on Computer Vision,2006,pp.16-29.
[49]E.Borenstein and S.Ullman,“Class-specific top-down segmentation,” in Proceedings of European Conference on Computer Vision,2002,pp.109-124.
[50]A.B.Torralba,K.P.Murphy,and W.T.Freeman,“Sharing features:Efficient boosting procedures for multiclass object detection,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2004.
[51]A.C.Berg,T.L.Berg,and J.Malik,“Shape matching and object recognition using low distortion correspondences,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005.
[52]J.M.Winn and N.Jojic,“Locus:Learning object classes with unsupervised segmentation,” in Proceedings of IEEE International Conference on Computer Vision,2005,pp.756-763.
[53]K.Kummamuru,R.Lotlikar,S.Roy,K.Singal,and R.Krishnapuram,“A hierarchical monothetic document clustering algorithm for summarization and browsing search results,” in Proceedings of International Conference on World Wide Web,2004,pp.658-665.
[54]D.J.Lawrie and W.B.Croft,“Generating hierarchical summaries for web searches,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2003,pp.457-458.
[55]C.H.Brooks and N.Montanez,“Improved annotation of the blogosphere via autotagging and hierarchical clustering,” in Proceedings of International Conference on World Wide Web,2006.
[56]S.-L.Chuang and L.-F.Chien,“Towards automatic generation of query taxonomy:A hierarchical query clustering approach,” in Proceedings of IEEE International Conference on Data Mining,2002.
[57]P.Cimiano,A.Hotho,and S.Staab,“Comparing conceptual,divisive and agglomerative clustering for learning taxonomies from text,” in Proceedings of Eureopean Conference on Artificial Intelligence,2004,pp.435-439.
[58]P.Ferragina and A.Gulli,“The anatomy of a hierarchical clustering engine for web-page,news and book snippets,” in Proceedings of IEEE International Conference on Data Mining, 2004,pp.395-398.
[59]——,“A personalized search engine based on web-snippet hierarchical clustering,”in Proceedings of International Conference on World Wide Web,2005,pp.801-810.
[60]A.Griffiths,L.A.Robinson,and P.Willett,“Hierarchical agglomerative clustering methods for automatic document classification,” Journal of Documentation,vol.3,1984.
[61]A.Brandt,S.McCormick,and J.Ruge,“Algebraic multigrid (amg)for automatic multigrid solutions with application to geodetic computations,”Fort Coolins,CO,Tech.Rep.,1982.
[62]P.Berkhin,“Survey of clustering data mining techniques,” Accrue Software,Tech.Rep.,2002.
[63]R.Xu and D.W.II,“Survey of clustering algorithms,”IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.16.
[64]Y.Zhao and G.Karypis,“Evaluation of hierarchical clustering algorithms for document datasets,” in Proceedings of ACM Conference on Information and Knowledge Management,2002,pp.515-524.
[65]S.Guha,R.Rastogi,and K.Shim,“Cure:An efficient clustering algorithm for large databases,” Information System,vol.26,2001.
[66]——,“Rock:A robust clustering algorithm for categorical attributes,”Information System,vol.25,2000.
[67]T.Zhang,R.Ramakrishnan,and M.Livny,“Birch:A new data clustering algorithm and its applications,” Data Mining and Knowledge Discovery,vol.1,1997.
[68]G.Karypis,E.-H.Han,and V.Kumar,“Chameleon:Hierarchical clustering using dynamic modeling,” IEEE Computer,vol.32,1999.
[69]D.R.Cutting,D.R.Karger,J.O.Pedersen,and J.W.Tukey,“Scatter/gather:a cluster-based approach to browsing large document collections,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,1992,pp.318-329.
[70]B.Larsen and C.Aone,“Fast and effective text mining using linear-time document clustering,” in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,1999,pp.16-22.
[71]M.Steinbach,G.Karypis,and V.Kumar,“A comparison of document clustering techniques,” in Proceedings of ACM SIGKDD Workshop on Text Mining,2000.
[72]M.Belkin and P.Niyogi,“Laplacian eigenmaps for dimensionality reduction and data repre- sentation,” Neural Computation,vol.15,2003.
[73]A.Y Ng,M.I.Jordan,and Y.Weiss,“On spectral clustering:Analysis and an algorithm,” in Advances in Neural Information Processing Systems,2001.
[74]P.K.Chan,M.D.F.Schlag,and J.Y.Zien,“Spectral k-way ratio-cut partitioning and clustering,” in Proceedings of Design Automation Conference,1993.
[75]J.Shi and J.Malik,“Normalized cuts and image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.22,no.8,pp.888-905,2000.
[76]C.H.Q.Ding,X.He,H.Zha,M.Gu,and H.D.Simon,“A min-max cut algorithm for graph partitioning and data clustering,” in Proceedings of IEEE International Conference on Data Mining,2001.
[77]F.Wang,C.Zhang,and T.Li,“Regularized clustering for documents,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2007.
[78]X.He,D.Cai,H.Liu,and W.-Y Ma,“Locality preserving indexing for document representation,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2004,pp.96-103.
[79]D.Cai,X.He,and J.Han,“Document clustering using locality preserving indexing,” IEEE Transactions on Knowledge and Data Engineering,vol.17,2005.
[80]W.Xu,X.Liu,and Y.Gong,“Document clustering based on non-negative matrix factorization,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2003,pp.267-273.
[81]C.Ding,T.Li,W.Peng,and H.Park,“Document clustering using locality preserving indexing,” Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2006.
[82]T.Li and C.H.Q.Ding,“The relationships among various nonnegative matrix factorization methods for clustering,” in Proceedings of IEEE International Conference on Data Mining,2006.
[83]T.Li,S.Ma,and M.Ogihara,“Document clustering via adaptive subspace iteration,”in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2004,pp.218-225.
[84]W.Xu and Y Gong,“Document clustering by concept factorization,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2004.
[85]F.Beil,M.Ester,and X.Xu,“Frequent term-based text clustering,” in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2002,pp.436-442.
[86]B.C.M.Fung,K.Wang,and M.Ester,“Hierarchical document clustering using frequent itemsets,” in Proceedings of the Third SIAM International Conference on Data Mining,2003.
[87]I.S.Dhillon,“Co-clustering documents and words using bipartite spectral graph partitioning,” in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2001.
[1]D.A.McAllester,M.Collins,and F.Pereira,"Case-factor diagrams for structured probabilistic modeling," in Proceedings of Conference on Uncertainty in Artificial Intelligence,2004,pp.382-391.
[2]J.D.Lafferty,A.McCallum,and F.C.N.Pereira,"Conditional random fields:Probabilistic models for segmenting and labeling sequence data," in Proceedings of International Conference on Machine Learning,2001,pp.282-289.
[3]D.Klein and C.D.Manning,"Natural language grammar induction using a constituent-context model," in Advances in Neural Information Processing Systems,2001,pp.35-42.
[4]R.Dechter and R.Mateescu,“And/or search spaces for graphical models,” Artificial Intelligence,vol.171,no.2-3,pp.73-106,2007.
[5]H.Chen,Z.Xu,Z.Liu,and S.C.Zhu,“Composite templates for cloth modeling and sketching,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2006.
[6]L.S.Zettlemoyer and M.Collins,“Learning to map sentences to logical form:Structured classification with probabilistic categorial grammars,” in Proceedings of Conference on Uncertainty in Artificial Intelligence,2005,pp.658-666.
[7]B.D.Ripley,Pattern Recognition and Neural Networks.New York,NY,USA:Cambridge University Press,1996.
[8]C.Manning and H.Schuetze,Foundations of statistical natural language processing.Cambridge,Mass,USA:MIT Press,1999.
[9]Z.Tu,X.Chen,A.L.Yuille,and S.C.Zhu,“Image parsing:Unifying segmentation,detection,and recognition,” in Proceedings of IEEE International Conference on Computer Vision,2003,pp.18-25.
[10]R.Fergus,P.Perona,and A.Zisserman,“Object class recognition by unsupervised scaleinvariant learning,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2003,pp.264-271.
[11]H.Barlow,“Unsupervised learning,” Neural Computation,vol.1,pp.295-311,1989.
[12]L.Fei-Fei,R.Fergus,and P.Perona,“Learning generative visual models from few training examples:An incremental bayesian approach tested on 101 object categories,” Proceedings of Conference on Computer Vision and Image Understanding,vol.106,pp.59-70,2007.
[13]R.Fergus,P.Perona,and A.Zisserman,“A sparse object category model for efficient learning and exhaustive recognition,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005,pp.380-387.
[14]D.J.Crandall and D.P.Huttenlocher,“Weakly supervised learning of part-based spatial models for visual object recognition,” in Proceedings of European Conference on Computer Vision,2006,pp.16-29.
[15]D.J.Crandall,P.F.Felzenszwalb,and D.P.Huttenlocher,“Spatial priors for part-based recognition using statistical models,”in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005,pp.10-17.
[16]G.Csurka,C.Dance,J.Willamowski,L.Fan,C.Bray,and G.Csurka,“Visual categoriza- tion with bags of keypoints,” in Proceedings of European Conference on Computer Vision International Workshop on Statistical Learning in Computer Vision,2004.
[17]M.Meila and M.I.Jordan,“Learning with mixtures of trees,” Journal of Machine Learning Research,vol.1,pp.1-48,2000.
[18]S.D.Pietra,V.J.D.Pietra,and J.D.Lafferty,“Inducing features of random fields,” IEEE Transactions on Pattern Analysis andMachine Intelligence,vol.19,no.4,pp.380-393,1997.
[19]S.C.Zhu,Y.N.Wu,and D.Mumford,“Minimax entropy principle and its application to texture modeling,” Neural Computation,vol.9,no.8,pp.1627-1660,1997.
[20]A.McCallum,“Efficiently inducing features of conditional random fields,” in Conference on Uncertainty in Artificial Intelligence,2003,pp.403-410.
[21]N.Friedman,“The bayesian structural em algorithm,”in Proceedings of Conference on Uncertainty in Artificial Intelligence,1998,pp.129-138.
[22]L.Shams and C.von der Malsburg,“Are object shape primitives learnable?”Neurocomputing,vol.26-27,pp.855-863,1999.
[23]J.Ponce,T.L.Berg,M.Everingham,D.A.Forsyth,M.Hebert,S.Lazebnik,M.Marszalek,C.Schmid,B.C.Russell,A.Torralba,C.K.I.Williams,J.Zhang,and A.Zisserman,“Dataset issues in object recognition,”in Toward Category-Level Object Recognition,2006,pp.29-48.
[24]F.V.Jensen,S.L.Lauritzen,and K.G.Olesen,“Bayesian updating in causal probabilistic networks by local computations,”Computational Statistics Quaterly,vol.4,pp.269-282,1990.
[25]T.Kadir and M.Brady,“Saliency,scale and image description,”International Journal of Computer Vision,vol.45,no.2,pp.83-105,2001.
[26]D.G.Lowe,“Distinctive image features from scale-invariant keypoints,”International Journal of Computer Vision,vol.60,no.2,pp.91-110,2004.
[27]S.Lazebnik,C.Schmid,and J.Ponce,“Semi-local affine parts for object recognition,” in Proceedings of IEEE International Workshop on Biologically Motivated Computer Vision,2004.
[28]Y.Amit and D.Geman,“A computational model for visual selection,”Neural Computation,vol.11,no.7,pp.1691-1715,1999.
[29]Y.Jin and S.Geman,“Context and hierarchy in a probabilistic image model,”in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2006,pp.2145-2152.
[30]J.M.Coughlan,A.L.Yuille,C.English,and D.Snow,“Efficient deformable template detection and localization without user initialization,” Proceedings of Conference on Computer Vision and Image Understanding,pp.303-319,2000.
[31]R.M.Neal and G.E.Hinton,“A view of the em algorithm that justifies incremental,sparse,and other variants,”Learning in graphical models,pp.355-368,1999.
[32]L.Zhu,Y.Chen,and A.L.Yuille,“Unsupervised learning of a probabilistic grammar for object detection and parsing,”in Advances in Neural Information Processing Systems,2006,pp.1617-1624.
[33]——,“Unsupervised learning of probabilistic grammar-markov models for object categories,” IEEE Transactions on Pattern Analysis and Machine Intelligence,2008.
[34]Y.Chen,L.Zhu,C.Lin,A.L.Yuille,and H.Zhang,“Rapid inference on a novel and/or graph for object detection,segmentation and parsing,”in Advances in Neural Information Processing Systems,2007.
[1]L.Zhu,Y.Chen,and A.L.Yuille,"Unsupervised learning of a probabilistic grammar for object detection and parsing," in Advances in Neural Information Processing Systems,2006, pp.1617-1624.
[2]L.Zhu,Y.Chen,and A,"Unsupervised learning of probabilistic grammar-markov models for object categories,"IEEE Transactions on Pattern Analysis and Machine Intelligence,2008.
[3]R.Fergus,P.Perona,and A.Zisserman,"Object class recognition by unsupervised scaleinvariant learning," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2003,pp.264-271.
[4]R.Fergus,P,"A sparse object category model for efficient learning and exhaustive recognition," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005,pp.380-387.
[5]D.J.Crandall and D.P.Huttenlocher,"Weakly supervised learning of part-based spatial models for visual object recognition," in Proceedings of European Conference on Computer Vision,2006,pp.16-29.
[6]B.Leibe,A.Leonardis,and B.Schiele,"Combined object categorization and segmentation with an implicit shape model,”in Proceedings of European Conference on Computer Vision'04 Workshop on Statistical Learning in Computer Vision,2004,pp.17-32.
[7]E.Borenstein and S.Ullman,“Learning to segment,” in Proceedings of European Conference on Computer Vision,2004,pp.315-328.
[8]A.Levin and Y.Weiss,“Learning to combine bottom-up and top-down segmentation,”in Proceedings of European Conference on Computer Vision,2006,pp.581-594.
[9]X.Ren,C.Fowlkes,and J.Malik,“Cue integration for figure/ground labeling,” in Advances in Neural Information Processing Systems,2005.
[10]J.M.Winn and N.Jojic,“Locus:Learning object classes with unsupervised segmentation,” in Proceedings of IEEE International Conference on Computer Vision,2005,pp.756-763.
[11]J.Sivic,B.C.Russell,A.A.Efros,A.Zisserman,and W.Freeman,“Discovering object categories in image collections,”in Proceedings of IEEE International Conference on Computer Vision,2005,pp.370-377.
[12]L.Cao and L.Fei-Fei,“Spatially coherent latent topic model for concurrent object segmentation and classification,” in Proceedings of IEEE International Conference on Computer Vision,2007.
[13]Y.Boykov and V.Kolmogorov,“An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,”in International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition,2001,pp.359-374.
[14]A.Blake,C.Rother,M.Brown,P.Perez,and P.H.S.Torr,“Interactive image segmentation using an adaptive gmmrf model,”in Proceedings of European Conference on Computer Vision,2004,pp.428-441.
[15]Y Boykov and M.-P.Jolly,“Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images,” in Proceedings of IEEE International Conference on Computer Vision,2001,pp.105-112.
[16]C.Rother,V.Kolmogorov,and A.Blake,“”grabcut”:interactive foreground extraction using iterated graph cuts,”ACM Transactions on Graphics,vol.23,no.3,pp.309-314,2004.
[17]T.Kadir and M.Brady,“Saliency,scale and image description,” International Journal of Computer Vision,vol.45,no.2,pp.83-105,2001.
[18]D.G.Lowe,“Distinctive image features from scale-invariant keypoints,”International Journal of Computer Vision,vol.60,no.2,pp.91-110,2004.
[19]M.P.Kumar,P.H.S.Torr,and A.Zisserman,“Obj cut,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005,pp.18-25.
[20]L.Fei-Fei,R.Fergus,and P.Perona,“Learning generative visual models from few training examples:An incremental bayesian approach tested on 101 object categories,”Proceedings of Conference on Computer Vision and Image Understanding,vol.106,pp.59-70,2007.
[1]S.-L.Chuang and L.-F.Chien,"Towards automatic generation of query taxonomy:A hierarchical query clustering approach," in Proceedings of IEEE International Conference on Data Mining,2002.
[2]P.Cimiano,A.Hotho,and S.Staab,"Comparing conceptual,divisive and agglomerative clustering for learning taxonomies from text," in Proceedings of Eureopean Conference on Artificial Intelligence,2004,pp.435-439.
[3]P.Clerkin,P.Cunningham,and C.Hayes,"Ontology discovery for the semantic web using hierarchical clustering," in Proceedings of European Conference on Machine Learning Workshop on Semantic Web Mining,2001.
[4]D.R.Cutting,D.R.Karger,J.O.Pedersen,and J.W.Tukey,"Scatter/gather:a cluster-based approach to browsing large document collections,”in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,1992,pp.318-329.
[5]K.Kummamuru,R.Lotlikar,S.Roy,K.Singal,and R.Krishnapuram,“A hierarchical monothetic document clustering algorithm for summarization and browsing search results,”in Proceedings of International Conference on World Wide Web,2004,pp.658-665.
[6]D.J.Lawrie and W.B.Croft,“Generating hierarchical summaries for web searches,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2003,pp.457-458.
[7]C.H.Brooks and N.Montanez,“Improved annotation of the blogosphere via autotagging and hierarchical clustering,” in Proceedings of International Conference on World Wide Web,2006.
[8]P.Ferragina and A.Gulli,“The anatomy of a hierarchical clustering engine for web-page,news and book snippets,” in Proceedings of IEEE International Conference on Data Mining,2004,pp.395-398.
[9]——,“A personalized search engine based on web-snippet hierarchical clustering,” in Proceedings of International Conference on World Wide Web,2005,pp.801-810.
[10]Y.Zhao and G.Karypis,“Evaluation of hierarchical clustering algorithms for document datasets,” in Proceedings of ACM Conference on Information and Knowledge Management,2002,pp.515-524.
[11]A.Brandt,S.McCormick,and J.Ruge,“Algebraic multigrid (amg)for automatic multigrid solutions with application to geodetic computations,” Fort Coolins,CO,Tech.Rep.,1982.
[12]B.Larsen and C.Aone,“Fast and effective text mining using linear-time document clustering,” in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,1999,pp.16-22.
[13]M.Steinbach,G.Karypis,and V.Kumar,“A comparison of document clustering techniques,” in Proceedings of ACM SIGKDD Workshop on Text Mining,2000.
[14]P.K.Chan,M.D.F.Schlag,and J.Y.Zien,“Spectral k-way ratio-cut partitioning and clustering,” in Proceedings of Design Automation Conference,1993.
[15]J.Shi and J.Malik,“Normalized cuts and image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.22,no.8,pp.888-905,2000.
[16]C.H.Q.Ding,X.He,H.Zha,M.Gu,and H.D.Simon,“A min-max cut algorithm for graph partitioning and data clustering,” in Proceedings of IEEE International Conference on Data Mining,2001.
[17]Y.Koren,L.Carmel,and D.Harel,“Ace:A fast multiscale eigenvectors computation for drawing huge graphs,” in Proceedings of IEEE Symposium on Information Visualization,2002,p.137.
[18]E.Sharon,M.Galun,D.Sharon,R.Basri,and A.Brandt,“Hierarchy and adaptivity in segmenting visual scenes,” Nature,pp.810-813,2006.
[19]E.Sharon,A.Brandt,and R.Basri,“Fast multiscale image segmentation,”in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2000,pp.1070-1077.
[20]X.He,D.Cai,H.Liu,and W.-Y.Ma,“Locality preserving indexing for document representation,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2004,pp.96-103.
[21]P.Pantel and D.Lin,“Document clustering with committees,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2002,pp.199-206.
[22]W.Xu,X.Liu,and Y.Gong,“Document clustering based on non-negative matrix factorization,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2003,pp.267-273.
[23]B.Mandhani,S.Joshi,and K.Kummamuru,“A matrix density based algorithm to hierarchically co-cluster documents and words,” in Proceedings of International Conference on World Wide Web,2003,pp.511-518.
[24]T.Li,S.Ma,and M.Ogihara,“Document clustering via adaptive subspace iteration,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2004,pp.218-225.
[25]N.Ueda and K.Saito,“Parametric mixture models for multi-labeled text,”in Advances in Neural Information Processing Systems,2003.
[26]R.Baeza-Yates and B.Ribeiro-Nero,Modern Information Retrieval.New York:Addison-Wesley,1999.
[27]Y.Huang and T.M.Mitchell,“Text clustering with extended user feedback,” in Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2006,pp.413-420.
[28]C.Leacock,Combining local context and wordnet similarity for word sense identification.Cambridge:MIT Press,1998.
[29]A.P.Bradley,“Use of the area under the roc curve in the evaluation of machine learning algorithms,”Pattern Recognition,vol.30,pp.1145-1159,1997.