Deep contrastive multi-view clustering with doubly enhanced commonality

Recently, deep multi-view clustering leveraging autoencoders has garnered significant attention due to its ability to simultaneously enhance feature learning capabilities and optimize clustering outcomes. However, existing autoencoder-based deep multi-view clustering methods often exhibit a tendency...

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Bibliographic Details
Published in:	Multimedia systems Vol. 30; no. 4; p. 196
Main Authors:	Yang, Zhiyuan, Zhu, Changming, Li, Zishi
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024 Springer Nature B.V
Subjects:	Clustering Commonality Computer Communication Networks Computer Graphics Computer Science Cryptology Data Storage Representation Datasets Dilution Learning Multimedia Information Systems Neural networks Operating Systems Regular Paper Semantics Training Autoencoder Unsupervised learning Deep multi-view clustering Contrastive learning
ISSN:	0942-4962, 1432-1882
Online Access:	Get full text
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