A Survey on Multi-View Clustering

TL;DR

This survey comprehensively reviews multi-view clustering methods, categorizing approaches, analyzing their relationships with related techniques, and discussing real-world applications and future research directions.

Contribution

It introduces a novel taxonomy of multi-view clustering approaches and provides an extensive analysis of current progress and open challenges in the field.

Findings

Proposed a new taxonomy for MVC approaches

Analyzed relationships between MVC and related learning paradigms

Discussed real-world applications and future research directions

Abstract

With advances in information acquisition technologies, multi-view data become ubiquitous. Multi-view learning has thus become more and more popular in machine learning and data mining fields. Multi-view unsupervised or semi-supervised learning, such as co-training, co-regularization has gained considerable attention. Although recently, multi-view clustering (MVC) methods have been developed rapidly, there has not been a survey to summarize and analyze the current progress. Therefore, this paper reviews the common strategies for combining multiple views of data and based on this summary we propose a novel taxonomy of the MVC approaches. We further discuss the relationships between MVC and multi-view representation, ensemble clustering, multi-task clustering, multi-view supervised and semi-supervised learning. Several representative real-world applications are elaborated. To promote future development of MVC, we envision several open problems that may require further investigation and thorough examination.