Diversity-Induced Multi-View Subspace Clustering

Xiaochun Cao, Changqing Zhang, Huazhu Fu, Si Liu, Hua Zhang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 586-594

Abstract


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 features, 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.

Related Material


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[bibtex]
@InProceedings{Cao_2015_CVPR,
author = {Cao, Xiaochun and Zhang, Changqing and Fu, Huazhu and Liu, Si and Zhang, Hua},
title = {Diversity-Induced Multi-View Subspace Clustering},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}