知识迁移的极大熵聚类算法及其在纹理图像分割中的应用
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摘要
本文研究了一种新型的基于知识迁移的极大熵聚类技术。拟解决两大挑战性问题:1)如何从源域中选择合适的知识对目标域进行迁移学习以最终强化目标域的聚类性能;2)若存在源域聚类数与目标域聚类数不一致的情况时,该如何进行迁移聚类。为此提出一种全新的迁移聚类机制,即基于聚类中心的中心匹配迁移机制。进一步将该机制与经典极大熵聚类算法相融合提出了基于知识迁移的极大熵聚类算法(KT-MEC)。实验表明,在不同迁移场景下的纹理图像分割应用中,KT-MEC算法较很多现有聚类算法具有更高的精确度和抗噪性。
In this paper,we propose a novel technique for maximum entropy clustering( MEC) based on knowledge transfer. More specifically,we aim to solve the following two challenging questions. First,how can knowledge be appropriately selected from a source domain to enhance clustering performance in the target domain via transfer learning? Second,how best do we conduct transfer clustering if the number of clusters in the source domain and the target domain are inconsistent? To address these questions,we designed a new transfer clustering mechanism called the central matching transfer mechanism, which we based on clustering centers. Further, we developed a knowledge-transfer-based maximum entropy clustering( KT-MEC) algorithm by incorporating our mechanism into the classic MEC approach. Our experimental results reveal that our proposed KT-MEC algorithm achieves a higher level of accuracy and better noise immunity than many existing methods when applied to texture image segmentation in different transfer scenarios.
In this paper,we propose a novel technique for maximum entropy clustering( MEC) based on knowledge transfer. More specifically,we aim to solve the following two challenging questions. First,how can knowledge be appropriately selected from a source domain to enhance clustering performance in the target domain via transfer learning? Second,how best do we conduct transfer clustering if the number of clusters in the source domain and the target domain are inconsistent? To address these questions,we designed a new transfer clustering mechanism called the central matching transfer mechanism, which we based on clustering centers. Further, we developed a knowledge-transfer-based maximum entropy clustering( KT-MEC) algorithm by incorporating our mechanism into the classic MEC approach. Our experimental results reveal that our proposed KT-MEC algorithm achieves a higher level of accuracy and better noise immunity than many existing methods when applied to texture image segmentation in different transfer scenarios.
引文
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[2]KIM S,YOO C D,NOWOZIN S,et al.Image segmentation usinghigher-order correlation clustering[J].IEEEtransactions on pattern analysis and machine intelligence,2014,36(9):1761-1774.
[3]JIANG Yizhang,CHUNG F L,WANG Shitong,et al.Collaborative fuzzy clustering from multiple weighted views[J].IEEE transactions on cybernetics,2015,45(4):688-701.
[4]BEZDEK J C.Pattern recognition with fuzzy objective function algorithms[M].USA:Springer Science&Business Media,2013:155-201.
[5]KRISHNAPURAM R,KELLER J M.A possibilistic approach to clustering[J].IEEE transactions on fuzzy systems,1993,1(2):98-110.
[6]KARAYIANNIS N B.MECA:maximum entropy clustering algorithm[C]//Proceedings of the Third IEEE Fuzzy Systems Conference.Orlando,USA:IEEE,1994:630-635.
[7]PAN S J,YANG Qiang.A survey on transfer learning[J].IEEE transactions on knowledge and data engineering,2010,22(10):1345-1359.
[8]DENG Zhaohong,CHOI K S,JIANG Yizhang,et al.Generalized hidden-mapping ridge regression,knowledgeleveraged inductive transfer learning for neural networks,fuzzy systems and kernel methods[J].IEEE transactions on cybernetics,2014,44(12):2585-2599.
[9]DENG Zhaohong,JIANG Yizhang,CHOI K S,et al.Knowledge-leverage-based TSK fuzzy system modeling[J].IEEE transactions on neural networks and learning systems,2013,24(8):1200-1212.
[10]ZHI Xiaobin,FAN Jiulun,ZHAO Feng.Fuzzy linear discriminant analysis-guided maximum entropy fuzzy clustering algorithm[J].Pattern recognition,2013,46(6):1604-1615.
[11]DAI Wenyuan,YANG Qiang,XUE Guirong,et al.Selftaught clustering[C]//Proceedings of the 25th International Conference on Machine Learning.New York,USA:ACM,2008:200-207.
[12]JIANG Wenhao,CHUNG F L.Transfer spectral clustering[M]//FLACH P A,DE BIE T,CRISTIANINI N.Machine Learning and Knowledge Discovery in Databases.Berlin Heidelberg:Springer,2012:789-803.
[13]钱鹏江,孙寿伟,蒋亦樟,等.知识迁移极大熵聚类算法[J].控制与决策,2015,30(6):1000-1006.QIAN Pengjiang,SUN Shouwei,JIANG Yizhang,et al.Knowledge transfer based maximum entropy clustering[J].Control and decision,2015,30(6):1000-1006.
[14]PEDRYCZ W,RAI P.Collaborative clustering with the use of Fuzzy C-Means and its quantification[J].Fuzzy sets and systems,2008,159(18):2399-2427.
[15]GU Quanquan,ZHOU Jie.Learning the shared subspace for multi-task clustering and transductive transfer classification[C]//Proceedings of the Ninth IEEEInternational Conference on Data Mining.Miami,USA:IEEE,2009:159-168.
[16]GU Quanquan,ZHOU Jie.Co-clustering on manifolds[C]//Proceedings of the 15th ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining.New York,USA:ACM,2009:359-368.
[17]RANDEN T.Brodatz texture[EB/OL].[2015-12-14].http://www.ux.uis.no/~tranden/brodatz.html.
[18]DENG Zhaohong,CHOI K S,CHUNG F L,et al.Enhanced soft subspace clustering integrating withincluster and between-cluster information[J].Pattern recognition,2010,43(3):767-781.
[19]KYRKI V,KAMARAINEN J K,KALVIAINEN H.Simple Gabor feature space for invariant object recognition[J].Pattern recognition letters,2004,25(3):311-318.