A Survey on Incomplete Multi-view Clustering

TL;DR

This survey reviews recent advances in incomplete multi-view clustering, highlighting frameworks, comparing methods, and discussing future research directions for clustering data with missing views.

Contribution

It provides a comprehensive summary of current research, unifies various methods under common frameworks, and offers insights into future challenges in incomplete multi-view clustering.

Findings

Unified frameworks for incomplete multi-view clustering

Comparative analysis of representative methods

Identification of open problems and future directions

Abstract

Conventional multi-view clustering seeks to partition data into respective groups based on the assumption that all views are fully observed. However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failure of the conventional multi-view clustering methods. Clustering on such incomplete multi-view data is referred to as incomplete multi-view clustering. In view of the promising application prospects, the research of incomplete multi-view clustering has noticeable advances in recent years. However, there is no survey to summarize the current progresses and point out the future research directions. To this end, we review the recent studies of incomplete multi-view clustering. Importantly, we provide some frameworks to unify the corresponding incomplete multi-view clustering methods, and make an in-depth comparative analysis for some representative methods from theoretical and experimental perspectives. Finally, some open problems in the incomplete multi-view clustering field are offered for researchers.