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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Vydané v:	Multimedia systems Ročník 30; číslo 4; s. 196
Hlavní autori:	Yang, Zhiyuan, Zhu, Changming, Li, Zishi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024 Springer Nature B.V
Predmet:	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
On-line prístup:	Získať plný text
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