Incomplete multi-view clustering via local and global co-regularization

Jiye Liang, Xiaolin Liu, Liang Bai, Fuyuan Cao, Dianhui Wang

Abstract: The incompleteness of multi-view data is a phenomenon associated with real-world data mining applications, which brings a huge challenge for multi-view clustering. Although various types of clustering methods, which try to obtain a complete and consensus clustering result from a latent subspace, have been developed to overcome this problem, most methods excessively rely on views-public instances to bridge the connection with view-private instances. When lacking sufficient views-public instances, existing methods fail to transmit the information among incomplete views effectively. To overcome this limitation, we propose an incomplete multi-view clustering algorithm via local and global co-regularization (IMVC-LG). In this algorithm, we define a new objective function that is composed of two terms: local clustering from each view and global clustering from multiple views, which constrain each other to exploit the local clustering information from different incomplete views and determine a global consensus clustering result, respectively. Furthermore, an iterative optimization method is proposed to minimize the objective function. Finally, we compare the proposed algorithm with other state-of-the-art incomplete multi-view clustering methods on several benchmark datasets to illustrate its effectiveness.