Neighbor group structure preserving based consensus graph learning for incomplete multi-view clustering

Abstract

In the area of clustering, multi-view clustering has drawn a lot of research attention by making full use of information from different views. In many practical applications, collecting complete multi-view data without missing views is sometimes expensive and impossible. Therefore, the study in incomplete multi-view clustering has become a trend in the field of clustering analysis. Graph learning-based approach is one of the most effective tools. The essence of graph learning is how to construct the affinity graph or similarity matrix, whose elements depict the similarity of the corresponding sample pairs. In this paper, we propose a new method, called Neighbor Group Structure Preserving-based Consensus Graph Learning (NGSP_CGL), to learn a high-quality consensus graph for incomplete multi-view clustering. Different from the existing graph learning-based works which only focus on the relationship between isolated sample pairs, NGSP_CGL seeks to explore the neighbor group structure corresponding to the nearest neighbor sets of sample pairs and designs a novel but simple nearest neighbor group structure embedding constraint so as to enhance the quality of consensus graph. The experimental results on several datasets demonstrate the effectiveness of NGSP_CGL.