TY - GEN
T1 - Diversity-induced Multi-view Subspace Clustering
AU - Cao, Xiaochun
AU - Zhang, Changqing
AU - Fu, Huazhu
AU - Liu, Si
AU - Zhang, Hua
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/14
Y1 - 2015/10/14
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84959230215
U2 - 10.1109/CVPR.2015.7298657
DO - 10.1109/CVPR.2015.7298657
M3 - 会议稿件
AN - SCOPUS:84959230215
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 586
EP - 594
BT - IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
PB - IEEE Computer Society
T2 - IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
Y2 - 7 June 2015 through 12 June 2015
ER -