Research Area:  Machine Learning

Abstract:

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.

Keywords:  
Clustering methods
Clustering results
Data feature
Graph-Based System
multi-view clustering

Author(s) Name:  Hao Wang, Yan Yang, Bing Liu

Publisher name:  Elsevier

DOI:  10.1016/j.knosys.2018.10.022

Volume Information:  Volume 163

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0950705118305082