A study of graph-based system for multi-view clustering

• Published in: Knowledge-based systems Vol. 163; pp. 1009 - 1019
• Main Authors: Wang, Hao, Yang, Yan, Liu, Bing, Fujita, Hamido
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.01.2019; Elsevier Science Ltd
• Subjects: Clustering; Data fusion; Feature extraction; Graph theory; Graph-based technology; Knowledge; Laplacian matrix; Matrix; Multi-view clustering; Rank constraint; State of the art; Weight; Graph-based technology; Data fusion; Multi-view clustering; Laplacian matrix; Rank constraint
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2018.10.022

This paper studies clustering of multi-view data, known as multi-view clustering. Among existing multi-view clustering methods, one representative category of methods is the graph-based approach. Despite its elegant and simple formulation, the graph-based approach has not been studied in terms of (a) the generalization of the approach or (b) the impact of different graph metrics on the clustering results. This paper extends this important approach by first proposing a general Graph-Based System (GBS) for multi-view clustering, and then discussing and evaluating the impact of different graph metrics on the multi-view clustering performance within the proposed framework. GBS works by extracting data feature matrix of each view, constructing graph matrices of all views, and fusing the constructed graph matrices to generate a unified graph matrix, which gives the final clusters. A novel multi-view clustering method that works in the GBS framework is also proposed, which can (1) construct data graph matrices effectively, (2) weight each graph matrix automatically, and (3) produce clustering results directly. Experimental results on benchmark datasets show that the proposed method outperforms state-of-the-art baselines significantly.