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

Abstract

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 diagonal representation matrix of previous ℓ1 or ℓ2 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 clustering performance. In this paper, based on the deep subspace clustering network, we integrate 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 proposed. 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.