A Survey on Multiview Clustering

Clustering is a machine learning paradigm of dividing sample subjects into a number of groups such that subjects in the same groups are more similar to those in other groups. With advances in information acquisition technologies, samples can frequently be viewed from different angles or in different modalities, generating multiview data. Multiview clustering (MVC), that clusters subjects into subgroups using multiview data, has attracted more and more attentions. Although MVC methods have been developed rapidly, there has not been enough survey to summarize and analyze the current progress. Therefore, we propose a novel taxonomy of the MVC approaches. Similar to other machine learning methods, we categorize them into generative and discriminative classes. In the discriminative class, based on the way of view integration, we split it further into five groups—common eigenvector matrix, common coefficient matrix, common indicator matrix, direct combination, and combination after projection. Furthermore, we relate MVC to other topics: multiview representation, ensemble clustering, multitask clustering, multiview supervised, and semisupervised learning. Several representative real-world applications are elaborated for practitioners. Some benchmark multiview datasets are introduced and representative MVC algorithms from each group are empirically evaluated to analyze how they perform on benchmark datasets. To promote future development of MVC approaches, we point out several open problems that may require further investigation and thorough examination. Impact Statement—Multiview clustering has gained the success in a variety of applications in the past decade. In order to obtain a comprehensive picture of the MVC development, we provide a new categorization of existing MVC methods and introduce the representative algorithms in each category. At last, we point out open problems that are worth investigating to advance the MVC study. More promising MVC methods to solve these open problems may appear following this review paper from which a large number of applications can benefit.