Diversity-induced Multi-view Subspace Clustering

In this paper, we focus on how to boost the multi-view clustering by exploring the complementary information among multi-view features. A multi-view clustering framework, called Diversity-induced Multi-view Subspace Clustering (DiMSC), is proposed for this task. In our method, we extend the existing subspace clustering into the multi-view domain, and utilize the Hilbert Schmidt Independence Criterion (HSIC) as a diversity term to explore the complementarity of multi-view representations, which could be solved efficiently by using the alternating minimizing optimization. Compared to other multi-view clustering methods, the enhanced complementarity reduces the redundancy between the multi-view representations, and improves the accuracy of the clustering results. Experiments on both image and video face clustering well demonstrate that the proposed method outperforms the state-of-the-art methods.

[1]  Hal Daumé,et al.  Co-regularized Multi-view Spectral Clustering , 2011, NIPS.

[2]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[3]  Ning Chen,et al.  Predictive Subspace Learning for Multi-view Data: a Large Margin Approach , 2010, NIPS.

[4]  Qiang Ji,et al.  Constrained Clustering and Its Application to Face Clustering in Videos , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Dacheng Tao,et al.  A Survey on Multi-view Learning , 2013, ArXiv.

[6]  René Vidal,et al.  Sparse subspace clustering , 2009, CVPR.

[7]  Sham M. Kakade,et al.  Multi-view clustering via canonical correlation analysis , 2009, ICML '09.

[8]  Stephen Lin,et al.  Automatic Optic Disc Detection in OCT Slices via Low-Rank Reconstruction , 2015, IEEE Transactions on Biomedical Engineering.

[9]  Bernhard Schölkopf,et al.  Measuring Statistical Dependence with Hilbert-Schmidt Norms , 2005, ALT.

[10]  Christoph H. Lampert,et al.  Correlational spectral clustering , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[12]  TaoDacheng,et al.  Large-Margin Multi-ViewInformation Bottleneck , 2014 .

[13]  Dacheng Tao,et al.  Large-Margin Multi-ViewInformation Bottleneck , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Richard H. Bartels,et al.  Algorithm 432 [C2]: Solution of the matrix equation AX + XB = C [F4] , 1972, Commun. ACM.

[15]  R. Vidal,et al.  Sparse Subspace Clustering: Algorithm, Theory, and Applications. , 2013, IEEE transactions on pattern analysis and machine intelligence.

[16]  Martha White,et al.  Convex Multi-view Subspace Learning , 2012, NIPS.

[17]  Dong Xu,et al.  Weighted Block-Sparse Low Rank Representation for Face Clustering in Videos , 2014, ECCV.

[18]  Hal Daumé,et al.  A Co-training Approach for Multi-view Spectral Clustering , 2011, ICML.

[19]  Yong Yu,et al.  Robust Recovery of Subspace Structures by Low-Rank Representation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Michael I. Jordan,et al.  Multiple Non-Redundant Spectral Clustering Views , 2010, ICML.

[21]  Jianjiang Feng,et al.  Smooth Representation Clustering , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Michael I. Jordan,et al.  Iterative Discovery of Multiple AlternativeClustering Views , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  René Vidal,et al.  Sparse Subspace Clustering: Algorithm, Theory, and Applications , 2012, IEEE transactions on pattern analysis and machine intelligence.

[24]  Christoph H. Lampert,et al.  Learning Multi-View Neighborhood Preserving Projections , 2011, ICML.

[25]  Shuicheng Yan,et al.  Multi-task low-rank affinity pursuit for image segmentation , 2011, 2011 International Conference on Computer Vision.

[26]  Xiaochun Cao,et al.  Video Face Clustering via Constrained Sparse Representation , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).

[27]  Junsong Yuan,et al.  Multi-feature Spectral Clustering with Minimax Optimization , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Mehryar Mohri,et al.  Learning Non-Linear Combinations of Kernels , 2009, NIPS.

[29]  Zhi-Hua Zhou,et al.  Analyzing Co-training Style Algorithms , 2007, ECML.

[30]  Massih-Reza Amini,et al.  A co-classification approach to learning from multilingual corpora , 2010, Machine Learning.

[31]  Aristidis Likas,et al.  Kernel-Based Weighted Multi-view Clustering , 2012, 2012 IEEE 12th International Conference on Data Mining.

[32]  Yuhong Guo,et al.  Convex Subspace Representation Learning from Multi-View Data , 2013, AAAI.

[33]  V. D. Sa.  Spectral Clustering with Two Views , 2007 .

[34]  Wei Tang,et al.  Clustering with Multiple Graphs , 2009, 2009 Ninth IEEE International Conference on Data Mining.