2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Boston, MA, USA
June 7, 2015 to June 12, 2015
Xiaochun Cao , School of Computer Science and Technology, Tianjin University, 300072, China
Changqing Zhang , School of Computer Science and Technology, Tianjin University, 300072, China
Huazhu Fu , School of Computer Engineering, Nanyang Technological University, Nanyang Avenue 639798, Singapore
Si Liu , State Key Laboratory of Information Security, IIE, Chinese Academy of Sciences, Beijing, 100093, China
Hua Zhang , School of Computer Science and Technology, Tianjin University, 300072, China
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.
X. Cao, C. Zhang, H. Fu, Si Liu and Hua Zhang, "Diversity-induced Multi-view Subspace Clustering," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015, pp. 586-594.