基于局部结构保持的自适应有序回归学习
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摘要
有序回归学习是一种在训练模型过程中保持数据间序关系的机器学习方法,在图像分类等领域有着广泛的应用.现有的有序回归模型通过先验知识获得了更优的性能,但是它们没有考虑数据内的局部结构信息.本文在有序回归学习的同时保持局部结构信息,并嵌入图像空间距离度量信息,提出了一种基于局部结构保持的自适应有序回归方法(SaLSP-LDLOR).通过对局部保持矩阵进行模糊自适应处理,获得了更好的鲁棒性.实验结果表明,SaLSP-LDLOR在有序图像分类的场景下具有更优的性能和良好的鲁棒性.
The ordinal regression learning is a kind of machine learning which preserves the order relations between data. It is widely used in image classification and other fields. Usually,there is some prior knowledge in the ordinal regression model,but the local structure information is not considered. Exploring such information can help to improve the effectiveness of classifiers. In this paper,we propose an improved adaptive ordinal regression method which is based on locality preserving structure(SaLSP-LDLOR),and the newly developed method considers embedding the spatial distance measurement information of the image. Experimental results with the standard data sets verify the effectiveness and the robustness of the proposed method.
The ordinal regression learning is a kind of machine learning which preserves the order relations between data. It is widely used in image classification and other fields. Usually,there is some prior knowledge in the ordinal regression model,but the local structure information is not considered. Exploring such information can help to improve the effectiveness of classifiers. In this paper,we propose an improved adaptive ordinal regression method which is based on locality preserving structure(SaLSP-LDLOR),and the newly developed method considers embedding the spatial distance measurement information of the image. Experimental results with the standard data sets verify the effectiveness and the robustness of the proposed method.
引文
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[20] TIAN Q,SONGCAN C.A novel ordinal learning strategy:ordinal nearest-centroid projection[J].Knowledge-based systems,2015,88:144-153.
[21] WANG L W,YAN Z,JUFU F.On the euclidean distance of images[J].IEEE transactions on pattern analysis and machine intelligence,2005,27(8):1334-1339.
[22] HE X F,PARTHA N.Locality preserving projections[J].Advances in neural information processing systems,Cambridge:MIT Press,2004,16:585-591.
[2] NGUYEN B,CARLOS M,BERNARD DE B.Distance metric learning for ordinal classification based on triplet constraints[J].Knowledge-based systems,2018,142:17-28.
[3] BENDER R,ULRICH G.Ordinal logistic regression in medical research[J].Journal of the royal college of physicians of london,1997,31(5):546-551.
[4] PéREZ O M,CRUZ R M,AYLLON T M D,et al.An organ allocation system for liver transplantation based on ordinal regression[J].Applied soft computing,2014,14(A):88-98.
[5] CHANG K Y,CHEN C S,HUNG Y P.Ordinal hyperplanes ranker with cost sensitivities for age estimation[C]//2011 IEEE conference on computer vision and pattern recognition,United States:Colorado springs,2011.
[6] DIKKERS H,LEON R.Support vector machines in ordinal classification:an application to corporate credit scoring[J].Neural network world,2005,15(6):491.
[7] KIM M Y,VLADIMIR P.Structured output ordinal regression for dynamic facial emotion intensity prediction[M].European conference on computer vision.Springer:Berlin,Heidelberg,2010.
[8] GUTIERREZ P,PEREZ O M,SANCHEZ M J,et al.Ordinal regression methods:survey and experimental study[J].IEEE transactions on knowledge and data engineering,2016,28(1):127-146.
[9] KRAMER S,WIDMER G,PFAHRINGER B,et al.Prediction of ordinal classes using regression trees[J].Fundamenta informaticae,2001,47(1/2):1-13.
[10] KOTSIANTIS S B,PANAGIOTIS P E.A cost sensitive technique for ordinal classification problems[C]//Third Hellenic conference on artificial intelligence.Greece:Samas,2004.
[11] PéREZ O M,PEDRO A G,CéSAR H M.Projection-based ensemble learning for ordinal regression[J].IEEE transactions on cybernetics,2014,44(5):681-694.
[12] SáNCHEZ M J,PEDRO A G,CESAR H M.Evolutionary ordinal extreme learning machine[C]//International Conference on Hybrid Artificial Intelligence Systems.Berlin,Heidelberg:Springer,2013.
[13] FERNáNDEZ N F,ANNALISA R,SANTE C.Ordinal neural networks without iterative tuning[J].IEEE transactions on neural networks and learning systems,2014,25(11):2075-2085.
[14] FRANK E,MARK H.A simple approach to ordinal classification[C]//European Conference on Machine Learning.Berlin,Heidelberg:Springer,2001.
[15] WAEGEMAN W,LUC B.An ensemble of weighted support vector machines for ordinal regression[J].International journal of computer systems science and engineering,2009,3(1):47-51.
[16] DENG W Y,ZHENG Q H,LIAN S,et al.Ordinal extreme learning machine[J].Neurocomputing,2010,74(1):447-456.
[17] HERBRICH R.Large margin rank boundaries for ordinal regression[J].Advances in large margin classifiers,2000:115-132.
[18] CHU W,SATHIYA K S.Support vector ordinal regression[J].Neural computation,2007,19(3):792-815.
[19] TIAN Q,SONGCAN C.Comparative study among three strategies of incorporating spatial structures to ordinal image regression[J].Neurocomputing,2014,136(20):152-161.
[20] TIAN Q,SONGCAN C.A novel ordinal learning strategy:ordinal nearest-centroid projection[J].Knowledge-based systems,2015,88:144-153.
[21] WANG L W,YAN Z,JUFU F.On the euclidean distance of images[J].IEEE transactions on pattern analysis and machine intelligence,2005,27(8):1334-1339.
[22] HE X F,PARTHA N.Locality preserving projections[J].Advances in neural information processing systems,Cambridge:MIT Press,2004,16:585-591.