Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (5): 115-122.DOI: 10.3778/j.issn.1002-8331.2010-0274

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Multi-view Clustering via Graph Convolutional Neural Network

LI Yongzhen, LIAO Husheng   

  1. 1.Information Department, Beijing University of Technology, Beijing 100124, China
    2.School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
  • Online:2021-03-01 Published:2021-03-02



  1. 1.北京工业大学 信息学部,北京 100124
    2.北京建筑大学 电气与信息工程学院,北京 100044


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



关键词: 多视角聚类, 图卷积神经网络, 相对熵