A Survey on Multi-View Clustering
It provides a systematic overview for researchers in machine learning and data mining, but it is incremental as it synthesizes existing work without introducing new methods or results.
This paper addresses the lack of a comprehensive survey on multi-view clustering (MVC) by reviewing common strategies for combining multiple views of data and proposing a novel taxonomy of MVC approaches, while also discussing related fields and applications.
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.