Low-Rank And Sparse Tensor Representation For Multi-View Subspace Clustering

Learning an effective affinity matrix as the input of spectral clustering to achieve promising multi-view clustering is a key issue of subspace clustering. In this paper, we propose a low-rank and sparse tensor representation (LRSTR) method that learns the affinity matrix through a self-representation tensor and retains the similarity information of the view dimensions for multi-view subspace clustering. Specifically, the proposed LRSTR method imposes the tensor nuclear norm and tensor sparse constraints on self-representation tensor to characterize the relationship between views. The optimization model is solved under the framework of alternating direction method of multiplier. Experimental results on four datasets show that the proposed LRSTR method is better than several state-of-the-art methods.