Generalized Latent Multi-View Subspace Clustering.

Zhang, C; Fu, H; Hu, Q; Cao, X; Xie, Y; Tao, D; Xu, D

Generalized Latent Multi-View Subspace Clustering.

Zhang, C Fu, H Hu, Q Cao, X Xie, Y Tao, D

Xu, D

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE transactions on pattern analysis and machine intelligence, 2020, 42, (1), pp. 86-99
Issue Date:: 2020-01

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Field	Value	Language
dc.contributor.author	Zhang, C
dc.contributor.author	Fu, H
dc.contributor.author	Hu, Q
dc.contributor.author	Cao, X
dc.contributor.author	Xie, Y
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449
dc.contributor.author	Xu, D
dc.date.accessioned	2021-03-30T03:35:16Z
dc.date.available	2021-03-30T03:35:16Z
dc.date.issued	2020-01
dc.identifier.citation	IEEE transactions on pattern analysis and machine intelligence, 2020, 42, (1), pp. 86-99
dc.identifier.issn	0162-8828
dc.identifier.issn	1939-3539
dc.identifier.uri	http://hdl.handle.net/10453/147647
dc.description.abstract	Subspace clustering is an effective method that has been successfully applied to many applications. Here, we propose a novel subspace clustering model for multi-view data using a latent representation termed Latent Multi-View Subspace Clustering (LMSC). Unlike most existing single-view subspace clustering methods, which directly reconstruct data points using original features, our method explores underlying complementary information from multiple views and simultaneously seeks the underlying latent representation. Using the complementarity of multiple views, the latent representation depicts data more comprehensively than each individual view, accordingly making subspace representation more accurate and robust. We proposed two LMSC formulations: linear LMSC (lLMSC), based on linear correlations between latent representation and each view, and generalized LMSC (gLMSC), based on neural networks to handle general relationships. The proposed method can be efficiently optimized under the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) framework. Extensive experiments on diverse datasets demonstrate the effectiveness of the proposed method.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/DP180103424
dc.relation.ispartof	IEEE transactions on pattern analysis and machine intelligence
dc.relation.isbasedon	10.1109/tpami.2018.2877660
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Generalized Latent Multi-View Subspace Clustering.
dc.type	Journal Article
utslib.citation.volume	42
utslib.location.activity	United States
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-30T03:35:14Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	42
utslib.citation.issue	1

Abstract:

Subspace clustering is an effective method that has been successfully applied to many applications. Here, we propose a novel subspace clustering model for multi-view data using a latent representation termed Latent Multi-View Subspace Clustering (LMSC). Unlike most existing single-view subspace clustering methods, which directly reconstruct data points using original features, our method explores underlying complementary information from multiple views and simultaneously seeks the underlying latent representation. Using the complementarity of multiple views, the latent representation depicts data more comprehensively than each individual view, accordingly making subspace representation more accurate and robust. We proposed two LMSC formulations: linear LMSC (lLMSC), based on linear correlations between latent representation and each view, and generalized LMSC (gLMSC), based on neural networks to handle general relationships. The proposed method can be efficiently optimized under the Augmented Lagrangian Multiplier with Alternating Direction Minimization (ALM-ADM) framework. Extensive experiments on diverse datasets demonstrate the effectiveness of the proposed method.

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http://hdl.handle.net/10453/147647