Robust Multi-View Clustering With a Unified Weight Learning Paradigm

Multi-view clustering, which exploits multi-view information to improve the clustering performance has attracted much attention in recent years. However, existing methods seldom consider the diverse quality of data points in different views, and assign each data point with the same importance for clustering. This way degrades the clustering performance due to the interference of low quality data points on the learned clustering indicators. In this paper, a novel robust multi-view clustering method with a unified weight learning paradigm is proposed to address this issue. The unified weight learning paradigm adaptively learns the quality of data points and the clustering capability of each view. Specifically, the reconstruction error of each data point in each view is treated as a factor to depict the quality of data point in this view. Afterwards, the clustering capability of each view is captured from the diverse quality of data points in each view. The clustering capability of each view in turn improves the learning process of data quality. An alternating iterative optimization algorithm with theoretical convergence guarantee and complexity analysis is designed to optimize the objective function. Experimental results on real-world benchmark datasets demonstrate the superiority of the proposed method.