Auto-Weighted Incomplete Multi-View Clustering

Abstract

Nowadays, multi-view clustering has attracted more and more attention, which provides a way to partition multi-view data into their corresponding clusters. Previous studies assume that each data instance appears in all views. However, in real-world applications, it is common that each view may contain some missing data instances, resulting in incomplete multi-view data. To address the incomplete multi-view clustering problem, we will propose an auto-weighted incomplete multi-view clustering method in this paper, which learns a common representation of the instances and an affinity matrix of the learned representation simultaneously in a unified framework. Learning the affinity matrix of the representation guides to learn a more discriminative and compact consensus representation for clustering. Moreover, by considering the impact of the significance of different views, an adaptive weighting strategy is designed to measure the importance of each view. An efficient iterative algorithm is proposed to optimize the objective function. Experimental results on various real-world datasets show that the proposed method can improve the clustering performance in comparison with the state-of-the-art methods in most cases.