Dual self-paced multi-view clustering

By utilizing the complementary information from multiple views, multi-view clustering (MVC) algorithms typically achieve much better clustering performance than conventional single-view methods. Although in this field, great progresses have been made in past few years, most existing multi-view clustering methods still suffer the following shortcomings: (1) most MVC methods are non-convex and thus are easily stuck into suboptimal local minima; (2) the effectiveness of these methods is sensitive to the existence of noises or outliers; and (3) the qualities of different features and views are usually ignored, which can also influence the clustering result. To address these issues, we propose dual self-paced multi-view clustering (DSMVC) in this paper. Specifically, DSMVC takes advantage of self-paced learning to tackle the non-convex issue. By applying a soft-weighting scheme of self-paced learning for instances, the negative impact caused by noises and outliers can be significantly reduced. Moreover, to alleviate the feature and view quality issues, we develop a novel feature selection approach in a self-paced manner and a weighting term for views. Experimental results on real-world data sets demonstrate the effectiveness of the proposed method. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

By proposing dual self-paced multi-view clustering (DSMVC) method, this paper addresses the non-convex issue and reduces the impact of noises and outliers by utilizing self-paced learning. Additionally, a novel feature selection approach and weighting term for views are developed to alleviate the issues of feature and view quality. Experimental results demonstrate the effectiveness of the proposed method.