Laplacian multiset canonical correlations for multiview feature extraction and image recognition

Yuan, YH; Li, Y; Shen, XB; Sun, QS; Yang, JL

Laplacian multiset canonical correlations for multiview feature extraction and image recognition

Yuan, YH Li, Y Shen, XB

Sun, QS Yang, JL

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Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2017, 76 (1), pp. 731 - 755
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Yuan, YH	en_US
dc.contributor.author	Li, Y	en_US
dc.contributor.author	Shen, XB https://orcid.org/0000-0001-8494-4532	en_US
dc.contributor.author	Sun, QS	en_US
dc.contributor.author	Yang, JL	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Multimedia Tools and Applications, 2017, 76 (1), pp. 731 - 755	en_US
dc.identifier.issn	1380-7501	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127014
dc.description.abstract	© 2015, Springer Science+Business Media New York. Multiset canonical correlation analysis (MCCA) aims at revealing the linear correlations among multiple sets of high-dimensional data. Therefore, it is only a linear multiview dimensionality reduction technique and such a linear model is insufficient to discover the nonlinear correlation information hidden in multiview data. In this paper, we incorporate the local structure information into MCCA and propose a novel algorithm for multiview dimensionality reduction, called Laplacian multiset canonical correlations (LapMCCs), which simultaneously considers local within-view and local between-view correlations by using nearest neighbor graphs. This makes LapMCC capable of discovering the nonlinear correlation information among multiview data by combining many locally linear problems together. Moreover, we also develop an orthogonal version of LapMCC to preserve the metric structure. The proposed LapMCC method is applied to face and object image recognition. The experimental results on AR, Yale-B, AT&T, and ETH-80 databases demonstrate the superior performance of LapMCC compared to existing multiview dimensionality reduction methods.	en_US
dc.relation.ispartof	Multimedia Tools and Applications	en_US
dc.relation.isbasedon	10.1007/s11042-015-3070-y	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.classification	Software Engineering	en_US
dc.title	Laplacian multiset canonical correlations for multiview feature extraction and image recognition	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	76	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
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
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	76	en_US

Abstract:

© 2015, Springer Science+Business Media New York. Multiset canonical correlation analysis (MCCA) aims at revealing the linear correlations among multiple sets of high-dimensional data. Therefore, it is only a linear multiview dimensionality reduction technique and such a linear model is insufficient to discover the nonlinear correlation information hidden in multiview data. In this paper, we incorporate the local structure information into MCCA and propose a novel algorithm for multiview dimensionality reduction, called Laplacian multiset canonical correlations (LapMCCs), which simultaneously considers local within-view and local between-view correlations by using nearest neighbor graphs. This makes LapMCC capable of discovering the nonlinear correlation information among multiview data by combining many locally linear problems together. Moreover, we also develop an orthogonal version of LapMCC to preserve the metric structure. The proposed LapMCC method is applied to face and object image recognition. The experimental results on AR, Yale-B, AT&T, and ETH-80 databases demonstrate the superior performance of LapMCC compared to existing multiview dimensionality reduction methods.

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http://hdl.handle.net/10453/127014