关键词: 多视角聚类, 图卷积神经网络, 相对熵

Abstract:

Aiming at discovering complementarity and consistency among multi-view data, a multi-view clustering based on Graph Convolutional neural Network（GCN） is proposed. By adding pairwise constraints on embeddings learned via the GCN on common sub-graphs of multiple views, consistency can be effectively measured. Through sharing the parameters of GCN, generating embeddings of each view based on their complete graphs, and concatenating those embeddings for multi-view embedding, complementarity will be explored. Besides, a Kullback-Leibler（KL） divergence based objective is designed to constrain the above embedding, leading to clustering oriented embedding learned. Experiments are conducted on five widely used datasets, achieving best clustering results, which clarifies the effectiveness of the method for exploring complementarity and consistency among multi-view data.

Key words: multi-view clustering, graph convolutional neural network, Kullback-Leibler divergence