Latent Representation Guided Multi-view Clustering

Huang, S; Tsang, IW; Xu, Z; Lv, J

Latent Representation Guided Multi-view Clustering

Huang, S Tsang, IW Xu, Z Lv, J

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2022, PP, (99), pp. 1-6
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Huang, S
dc.contributor.author	Tsang, IW
dc.contributor.author	Xu, Z
dc.contributor.author	Lv, J
dc.date.accessioned	2023-03-22T23:47:05Z
dc.date.available	2023-03-22T23:47:05Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2022, PP, (99), pp. 1-6
dc.identifier.issn	1041-4347
dc.identifier.issn	1558-2191
dc.identifier.uri	http://hdl.handle.net/10453/168099
dc.description.abstract	Multi-view clustering aims to reveal the correlation between different input modalities in an unsupervised way. Similarity between data samples can be described by a similarity graph, which governs the quality of multi-view clustering. However, existing multi-view graph learning methods mainly construct similarity graph based on raw features, which are unreliable as real-world datasets usually contain noises, outliers, or even redundant information. In this paper, we formulate a novel model to simultaneously learn a robust structured similarity graph and perform multi-view clustering. The similarity graph is adaptively learned based on a latent representation that is invulnerable to noises and outliers. Furthermore, the similarity graph is enforced to contain a clear structure, i.e., the number of connected components of the target graph is exactly equal to the ground-truth class number. Consequently, the label to each data sample can be directly assigned without any postprocessing. As a result, our model aims at accomplishing three subtasks: latent representation extraction, similarity graph learning, and cluster label allocation, in a unified framework. These three subtasks are seamlessly integrated and can be mutually boosted by each other towards the overall optimal solution. An efficient alternation algorithm is proposed to solve the optimization problem. Experimental results on several benchmark datasets illustrate the effectiveness of the proposed model.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering
dc.relation.isbasedon	10.1109/TKDE.2022.3192686
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.title	Latent Representation Guided Multi-view Clustering
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-22T23:47:02Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Multi-view clustering aims to reveal the correlation between different input modalities in an unsupervised way. Similarity between data samples can be described by a similarity graph, which governs the quality of multi-view clustering. However, existing multi-view graph learning methods mainly construct similarity graph based on raw features, which are unreliable as real-world datasets usually contain noises, outliers, or even redundant information. In this paper, we formulate a novel model to simultaneously learn a robust structured similarity graph and perform multi-view clustering. The similarity graph is adaptively learned based on a latent representation that is invulnerable to noises and outliers. Furthermore, the similarity graph is enforced to contain a clear structure, i.e., the number of connected components of the target graph is exactly equal to the ground-truth class number. Consequently, the label to each data sample can be directly assigned without any postprocessing. As a result, our model aims at accomplishing three subtasks: latent representation extraction, similarity graph learning, and cluster label allocation, in a unified framework. These three subtasks are seamlessly integrated and can be mutually boosted by each other towards the overall optimal solution. An efficient alternation algorithm is proposed to solve the optimization problem. Experimental results on several benchmark datasets illustrate the effectiveness of the proposed model.

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http://hdl.handle.net/10453/168099