Joint Low-rank and Orthogonal Deep Multi-view Subspace Clustering based on Local Fusion

Abstract

In recent years, a number of multi-view clustering methods have been proposed through a global fusion paradigm. These methods take the entire sample space as the fusion object, where the global complementarity between views is explored and exploited to improve the clustering performance. However, local structures with strong or weak clustering capacity could coexist in each view. The traditional global fusion paradigm ignores the differences in clustering capacity of local structures, which makes it impossible to explore and exploit local complementarity between views. In this paper, a novel deep multi view subspace clustering method based on local fusion is proposed to solve this problem. First, a low rank self-expression layer is inserted into the deep autoencoder to eliminate the influence of noises when obtaining local cluster structure. Then, the fusion object is refined from the entire sample space to the local cluster structure, where a self-weighted strategy is designed to assign contribution weight according to the clustering capacity of the local cluster structure. Meanwhile, we joint orthogonal constraint to enhance the discriminative of local cluster structure that is more suitable for downstream clustering task. Experiments on several real-world datasets show that the proposed method achieves better clustering performance than most traditional multi-view clustering methods based on global fusion.