Neighbor-aware deep multi-view clustering via graph convolutional network.

Multi-view clustering (MVC) enhances the clustering performance of data by combining correlation information from different views. However, most existing MVC approaches process each sample independently and ignore the correlation amongst samples, resulting in reduced clustering performance. Although graph convolution network (GCN) can naturally capture correlation amongst samples by integrating the neighbors and structural information into representation learning, it is used in the semi-supervised learning scenario. In this paper, we propose a neighbor-aware deep MVC framework based on GCN (NMvC-GCN) for clustering multi-view samples and training GCN in a fully unsupervised manner. In addition, we design a consensus regularization to learn the common representations and introduce a clustering embedding layer to jointly optimize the clustering task and representation learning, so that the correlation amongst samples and that between the clustering task and representation learning can be fully explored. Extensive experiments on 10 datasets illustrate that NMvC-GCN significantly outperforms the state-of-the-art MVC methods. Our code will be released at https://github.com/dugzzuli/NMvC-GCN. • Graph Convolution Networks are adopted to fuse topological structure and sample features. • An unsupervised way is proposed to jointly train GCN of each view. • Consensus regularization pushes data from each view to generate consensus representation. • Experiments with real datasets are carried out to evaluate our methods. [ABSTRACT FROM AUTHOR]