Coupled Learning for Kernel Representation and Graph Tensor in Multi-view Subspace Clustering

Mansheng Chen, Chang-Dong Wang, Dong Huang, Jian-Huang Lai

Abstract: An increasing amount of attention have been attracted in multi-view subspace clustering, whose impressive performance can be achieved by means of the self-expressive property, under the assumption of linear relations between multi-view data samples. However, most of them fail to recover the nonlinear relations between multi-view data for deeper study, and additionally they are incapable of discovering the comprehensiveness and higher-order correlations among multiple views. To deal with these challenges, we propose a novel model termed Coupled Learning for Kernel Representation and Graph Tensor (CLKT) in Multi-view Subspace Clustering, where both nonlinear relations and higher-order correlations among multiple affinity graphs are jointly learned in a unified framework. Optimal solutions of the proposed method can be obtained by an alternative minimizing optimization strategy. Extensive experiments on six real-world datasets indicate the superiority of CLKT compared with the state-of-the-art methods.