Multi-view subspace clustering network with block diagonal and diverse representation

Learning multi-view data, and especially multi-view data clustering, is a popular area in computer vision and pattern recognition. The multi-view subspace clustering has achieved a better clustering quality than the single-view subspace clustering, mainly because of the complementarity of multi-view information. First, for not directly pursuing a block diago-nal representation matrix of previous 21 or 22 regularizers in a deep subspace clustering network, a k-block diagonal regularizer is proposed to replace traditional regularizers. This block diagonal representation module is integrated into this multi-view subspace clustering network, and it can improve the clustering quality. Secondly, there exists some redundancy among the representation matrices, and a diverse representation module can be introduced into this network. This can boost the diversity of representation matrices, and make learned representation matrices more discriminative and help improve the clus-tering performance. In this paper, based on the deep subspace clustering network, we inte-grate the block diagonal and diverse representation into the network, and a multi-view subspace clustering network with the block diagonal and the diverse representation is pro-posed. The experimental results on the UCI Digit, Caltech101-20, COIL100 and Caltech101-7 datasets have demonstrated the superior performance of the proposed algorithm over other popular multi-view algorithms. (c) 2023 Elsevier Inc. All rights reserved.