Anchor-based multi-view subspace clustering with graph learning

Abstract

Multi-view subspace clustering (MVSC) has been drawn wide attentions in the area of pattern recognition and data mining. However, for a multi-view dataset with n samples and V views from k clusters, MVSC commonly requires O(Vn2) memory for storing the view-specific graph matrices and O(n3) time for the eigenvalue decomposition of a shared graph matrix. Hence, most of MVSC methods are difficult to handle the large-scale multi-view data problem. To address this issue, this paper proposes an Anchor-based Multi-View Subspace Clustering with Graph Learning (AMVSCGL) method. Instead of constructing a n×n graph matrix, our method generates a shared coefficient matrix with the size of n×k based on few learned view-specific anchors. Moreover, through further merging a graph learning term, this shared coefficient matrix can simultaneously capture the global and local information among multiple views and few learned view-specific anchors for clustering. Experimental results on seven large-scale multi-view data verify our AMVSCGL’s effectiveness and superiority in comparison with some state-of-the-art methods.