A Comprehensive Survey on Multi-view Clustering

The development of information gathering and extraction technology has led to the popularity of multi-view data, which enables samples to be seen from numerous perspectives. Multi-view clustering (MVC), which groups data samples by leveraging complementary and consensual information from several vie...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2023-12, Vol.35 (12), p.1-20
Main Authors: Fang, Uno, Li, Man, Li, Jianxin, Gao, Longxiang, Jia, Tao, Zhang, Yanchun
Format: Article
Language:English
Subjects:
Artificial neural networks
Clustering
Contrastive Learning
Data mining
Deep Representation Learning
Feature extraction
Graph learning
Graphical representations
Learning
Multi-view Clustering
Neural networks
Nonnegative Matrix Factorisation
Representation learning
Task analysis
Taxonomy
Tensors
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Summary:The development of information gathering and extraction technology has led to the popularity of multi-view data, which enables samples to be seen from numerous perspectives. Multi-view clustering (MVC), which groups data samples by leveraging complementary and consensual information from several views, is gaining popularity. Despite the rapid evolution of MVC approaches, there has yet to be a study that provides a full MVC roadmap for both stimulating technical improvements and orienting research newbies to MVC. In this article, we review recent MVC techniques with the purpose of exhibiting the concepts of popular methodologies and their advancements. This survey not only serves as a unique MVC comprehensive knowledge for researchers but also has the potential to spark new ideas in MVC research. We summarise a large variety of current MVC approaches based on two technical mechanisms: heuristic-based multi-view clustering (HMVC) and neural network-based multi-view clustering (NNMVC). We end with four technological approaches within the category of HMVC: nonnegative matrix factorisation, graph learning, latent representation learning, and tensor learning. Deep representation learning and deep graph learning are two technical methods that we demonstrate in NNMVC. We also show 15 publicly available multi-view datasets and examine how representative MVC approaches perform on them. In addition, this study identifies the potential research directions that may require further investigation in order to enhance the further development of MVC.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2023.3270311