Deep multi-view spectral clustering via ensemble

Abstract

Graph-based methods have achieved great success in multi-view clustering. However, existing graph-based models generally utilize shallow and linear embedding functions to obtain the common spectral embedding for clustering assignments. In addition, the fusion similarity graphs from multiple views are generally obtained by a simple weighted-sum rule. To this end, we propose a novel deep multi-view spectral clustering via ensemble model (DMCE), which applies ensemble clustering to fuse the similarity graphs from different views. On this basis, we employ the graph auto-encoder to learn the common spectral embedding, which can be regarded as the indicator matrix directly. Moreover, a unified optimization framework is designed to update the variables in the proposed DMCE, which consists of graph reconstruction loss, orthogonal loss, and graph contrastive learning loss. Extensive experiments on six real-world benchmark datasets have demonstrated the effectiveness of our model compared with the state-of-the-art multi-view clustering methods.