Online Binary Incomplete Multi-View Clustering

Abstract

Multi-view clustering has attracted considerable attention in the past decades, due to its good performance on the data with multiple modalities or from diverse sources. In real-world applications, multi-view data often suffer from incompleteness of instances. Clustering on such multi-view data is called incomplete multi-view clustering (IMC). Most of the existing IMC solutions are offline and have high computational and memory costs especially for large-scale datasets. To tackle these challenges, in this paper, we propose a Online Binary Incomplete Multi-view Clustering (OBIMC) framework. OBIMC robustly learns the common compact binary codes for incomplete multi-view features. Moreover, the cluster structures are optimized with the binary codes in an online fashion. Further, we develop an iterative algorithm to solve the resultant optimization problem with linear computational complexity and theoretically prove its convergence. Experiments on four real datasets demonstrate the efficiency and effectiveness of the proposed OBIMC method. As indicated, our algorithm significantly and consistently outperforms some state-of-the-art algorithms with much less running time.