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Multi-view clustering via pairwise sparse subspace representation.pdf
Neurocomputing 156 (2015) 12–21
Contents lists available at ScienceDirect
Neurocomputing
journal homepage: /locate/neucom
Multi-view clustering via pairwise sparse subspace representation
Qiyue Yin, Shu Wu, Ran He, Liang Wang n
Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China
article info
Article history: Received 26 September 2014 Received in revised form 4 January 2015 Accepted 4 January 2015 Communicated by Yi Zhe Song Available online 19 January 2015
Keywords: Multi-view clustering Subspace clustering Pairwise sparse representation
abstract
Multi-view clustering, which aims to cluster datasets with multiple sources of information, has a wide range of applications in the communities of data mining and pattern recognition. Generally, it makes use of the complementary information embedded in multiple views to improve clustering performance. Recent methods usually ?nd a low-dimensional embedding of multi-view data, but often ignore some useful prior information that can be utilized to better discover the latent group structure of multi-view data. To alleviate this problem, a novel pairwise sparse subspace representation model for multi-view clustering is proposed in this paper. The objective function of our model mainly includes two parts. The ?rst part aims to harness prior information to achieve a sparse representation of each high-dimensional data point with respect to other data points in the same view. The second part aims to maximize the correlation between the representations of different views. An alternating minimization method is provided as an ef?cient solution for the proposed multi-view clustering algorithm. A detailed theoretical analysis is also conducted to guarantee the convergence of the proposed method. Moreover, we show that the must-link and cannot-link constraints can be naturally integrated into the proposed mo
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