Unrestricted Anchor Graph based GCN for Incomplete Multi-view Clustering

In recent years, the task of multi-view clustering(MVC) has attracted more and more attention. Meanwhile, the graph convolution network(GCN) based MVC method has made consistent achievements in processing graph-structured data. However, real world data often suffers from missing some instances in each view, leading to the problem of incomplete multi-view clustering. It's a really challenge to capture the graph structure of incomplete views for GCN to process, especially in the high missing-rate situation. To address this issue, this paper proposes a novel and effective graph structure method called unrestricted anchor graph(UAG). Moreover, an Unrestricted Anchor Graph based GCN framework(UAGCN) is designed for incomplete multi-view clustering. Specifically, our method employs the unrestricted anchor to reconstruct the relationship in high missing-rate data to describe the graph structure, and then integrates GCN to obtain the graph embedding of incomplete data for clustering. The experimental results on multiple data sets show that our method is superior to comparison methods.