Self-representation and matrix factorization based multi-view clustering

Although the promising clustering performance, existing self-representation based multi-view subspace clustering methods directly minimize the divergence between affinity matrices to learn the consensus affinity matrix. This does not make sense for multi-view clustering due to the facts that multi-view data are often a collection of distinct attributes of the objects, and each view includes some contents of the objects that other views do not. Thus, the learned affinity representation is sub-optimal and cannot well characterize the cluster structure. To handle this problem, drawing the inspiration from matrix factorization, which lends embedding representation to clustering interpretation, we propose a novel multi-view subspace clustering method. Our method learns affinity representation between data by joint selfrepresentation and matrix factorization with weighted tensor Schatten p-norm constraint. Moreover, auto-weighted strategy is introduced to adaptively characterize the difference between singular values to improve the stableness of the algorithm. To further characterize class-specificity distribution, which well encodes cluster structure, we employ the l(1;2)-norm regularization on affinity representation. Experimental results on several data sets indicate that our method outperforms state-of-the-art selfrepresentation based multi-view subspace clustering methods. (C) 2021 Elsevier B.V. All rights reserved.