Representation Learning in Multi-view Clustering: A Literature Review

Multi-view clustering (MVC) has attracted more and more attention in the recent few years by making full use of complementary and consensus information between multiple views to cluster objects into different partitions. Although there have been two existing works for MVC survey, neither of them joi...

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Vydáno v:Data Science and Engineering Ročník 7; číslo 3; s. 225 - 241
Hlavní autoři: Chen, Man-Sheng, Lin, Jia-Qi, Li, Xiang-Long, Liu, Bao-Yu, Wang, Chang-Dong, Huang, Dong, Lai, Jian-Huang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Springer Nature Singapore 01.09.2022
Springer
Springer Nature B.V
Témata:
Algorithm Analysis and Problem Complexity
Algorithms
Analysis
Artificial Intelligence
Chemistry and Earth Sciences
Clustering
Computer Science
Data Mining and Knowledge Discovery
Data structures
Database Management
Deep learning
Literature reviews
Physics
Representations
Source code
Statistics for Engineering
Survey Paper
Surveys
Systems and Data Security
Taxonomy
China
Representation learning
Multi-view clustering
Shallow model
Deep model
Data mining
ISSN:2364-1185, 2364-1541
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Shrnutí:Multi-view clustering (MVC) has attracted more and more attention in the recent few years by making full use of complementary and consensus information between multiple views to cluster objects into different partitions. Although there have been two existing works for MVC survey, neither of them jointly takes the recent popular deep learning-based methods into consideration. Therefore, in this paper, we conduct a comprehensive survey of MVC from the perspective of representation learning. It covers a quantity of multi-view clustering methods including the deep learning-based models, providing a novel taxonomy of the MVC algorithms. Furthermore, the representation learning-based MVC methods can be mainly divided into two categories, i.e., shallow representation learning-based MVC and deep representation learning-based MVC, where the deep learning-based models are capable of handling more complex data structure as well as showing better expression. In the shallow category, according to the means of representation learning, we further split it into two groups, i.e., multi-view graph clustering and multi-view subspace clustering. To be more comprehensive, basic research materials of MVC are provided for readers, containing introductions of the commonly used multi-view datasets with the download link and the open source code library. In the end, some open problems are pointed out for further investigation and development.
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ISSN:2364-1185
2364-1541
DOI:10.1007/s41019-022-00190-8