Accurate Multi-view Clustering by Exploiting Within-view High-order Affinities through Tensor Self-representation

Abstract

Multi-view clustering divides data into their under-lying partitions by exploiting multiple views information. Popular approaches leverage cross-view information by self-expressive tensor learning and then learn a low-rank or sparse essential representation tensor for capturing the global structure of multi-view data. However, this process may encounter instability due to the lack of protection for local within-view structures. To overcome this problem, this paper proposes a unified L ow-rank and HyperGraph Laplacian regularized Tensor learning (LHGT) method for multi-view clustering, which aims to integrate within-view high-order affinities in self-expressive tensor learning for capturing inherent clustering structure. LHGT effectively extracts global cross-view and local within-view high-order statistics. An effective optimization procedure is tailored for the proposed model. Experimental results on six real-world datasets illustrate the efficacy of LHGT, where a clear advance over nine state-of-the-art approaches.