One-Stage Incomplete Multi-view Clustering via Late Fusion

Abstract

As a representative of multi-view clustering (MVC), late fusion MVC (LF-MVC) algorithm has attracted intensive attention due to its superior clustering accuracy and high computational efficiency. One common assumption adopted by existing LF-MVC algorithms is that all views of each sample are available. However, it is widely observed that there are incomplete views for partial samples in practice. In this paper, we propose One-Stage Late Fusion Incomplete Multi-view Clustering (OS-LF-IMVC) to address this issue. Specifically, we propose to unify the imputation of incomplete views and the clustering task into a single optimization procedure, so that the learning of the consensus partition matrix can directly assist the final clustering task. To optimize the resultant optimization problem, we develop a five-step alternate strategy with theoretically proved convergence. Comprehensive experiments on multiple benchmark datasets are conducted to demonstrate the efficiency and effectiveness of the proposed OS-LF-IMVC algorithm.