Multi-view common component discriminant analysis for cross-view classification

You, X; Xu, J; Yuan, W; Jing, XY; Tao, D; Zhang, T

Multi-view common component discriminant analysis for cross-view classification

You, X Xu, J Yuan, W Jing, XY Tao, D

Zhang, T

Permalink

Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Pattern Recognition, 2019, 92, (arXiv preprint arXiv:1304.5634 2013), pp. 37-51
Issue Date:: 2019-08-01

Closed Access

	Filename	Description	Size
	1-s2.0-S0031320319301074-main.pdf	Published version	2.77 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	You, X
dc.contributor.author	Xu, J
dc.contributor.author	Yuan, W
dc.contributor.author	Jing, XY
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449
dc.contributor.author	Zhang, T
dc.date.accessioned	2020-05-01T02:55:56Z
dc.date.available	2020-05-01T02:55:56Z
dc.date.issued	2019-08-01
dc.identifier.citation	Pattern Recognition, 2019, 92, (arXiv preprint arXiv:1304.5634 2013), pp. 37-51
dc.identifier.issn	0031-3203
dc.identifier.issn	1873-5142
dc.identifier.uri	http://hdl.handle.net/10453/140385
dc.description.abstract	© 2019 Cross-view classification that means to classify samples from heterogeneous views is a significant yet challenging problem in computer vision. An effective solution to this problem is the multi-view subspace learning (MvSL), which intends to find a common subspace for multi-view data. Although great progress has been made, existing methods usually fail to find a suitable subspace when multi-view data lies on nonlinear manifolds, thus leading to performance deterioration. To circumvent this drawback, we propose Multi-view Common Component Discriminant Analysis (MvCCDA) to handle view discrepancy, discriminability and nonlinearity in a joint manner. Specifically, our MvCCDA incorporates supervised information and local geometric information into the common component extraction process to learn a discriminant common subspace and to discover the nonlinear structure embedded in multi-view data. Optimization and complexity analysis of MvCCDA are also presented for completeness. Our MvCCDA is competitive with the state-of-the-art MvSL based methods on four benchmark datasets, demonstrating its superiority.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Pattern Recognition
dc.relation.isbasedon	10.1016/j.patcog.2019.03.008
dc.rights	info:eu-repo/semantics/restrictedAccess
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	Multi-view common component discriminant analysis for cross-view classification
dc.type	Journal Article
utslib.citation.volume	92
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/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-05-01T02:55:53Z
pubs.issue	arXiv preprint arXiv:1304.5634 2013
pubs.publication-status	Published
pubs.volume	92
utslib.start-page	37
utslib.citation.issue	arXiv preprint arXiv:1304.5634 2013

Abstract:

© 2019 Cross-view classification that means to classify samples from heterogeneous views is a significant yet challenging problem in computer vision. An effective solution to this problem is the multi-view subspace learning (MvSL), which intends to find a common subspace for multi-view data. Although great progress has been made, existing methods usually fail to find a suitable subspace when multi-view data lies on nonlinear manifolds, thus leading to performance deterioration. To circumvent this drawback, we propose Multi-view Common Component Discriminant Analysis (MvCCDA) to handle view discrepancy, discriminability and nonlinearity in a joint manner. Specifically, our MvCCDA incorporates supervised information and local geometric information into the common component extraction process to learn a discriminant common subspace and to discover the nonlinear structure embedded in multi-view data. Optimization and complexity analysis of MvCCDA are also presented for completeness. Our MvCCDA is competitive with the state-of-the-art MvSL based methods on four benchmark datasets, demonstrating its superiority.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/140385