Multi-View Subspace Clustering with Block Diagonal Representation

Abstract

Self-representation model has made good progress for a single view subspace clustering. This paper proposed the multi-view subspace clustering model based on self-representation. This model assumes that the samples from different classes are embedded in independent subspaces. Thus, the fused multi-view self-representation feature should be block diagonal, and a block diagonal regularizer with the complementarity of multi-view information is given. The model optimization algorithm by alternating minimization is proposed and its convergence without any additional assumption is proved. With the complementarity of multi-view information and the block diagonal property, our model will depict data more comprehensively than single view independently. The extensive experiments on public datasets demonstrate the effectiveness of our proposed model.