Manifold elastic net for sparse learning

Zhou, T; Tao, D

Manifold elastic net for sparse learning

Zhou, T Tao, D

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Publication Type:: Conference Proceeding
Citation:: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 3699 - 3704
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Zhou, T	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 3699 - 3704	en_US
dc.identifier.isbn	9781424427949	en_US
dc.identifier.issn	1062-922X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17284
dc.description.abstract	In this paper, we present the manifold elastic net (MEN) for sparse variable selection. MEN combines merits of the manifold regularization and the elastic net regularization, so it considers both the nonlinear manifold structure of a dataset and the sparse property of the redundant data representation. Face based gender recognition has received much attention in the psychophysical and video surveillance literatures. Most of existing works apply the appearance based information for data representation. A face image with size 40 by 40 could be seen as a point in a linear space with 1600 dimensions. For gender recognition, we have two classes (male and female) in total, so it is essential to find a small number of variables for representation to generalize duly. MEN can duly find the intrinsic structure of a dataset for separating males from the females. Sufficient experimental results on FERET and UMIST datasets suggest that MEN is more effective in selecting discriminative variables for face based gender recognition compared to principal component analysis, sparse principal component analysis, and discriminative locality alignment. ©2009 IEEE.	en_US
dc.relation.ispartof	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics	en_US
dc.relation.isbasedon	10.1109/ICSMC.2009.5346879	en_US
dc.title	Manifold elastic net for sparse learning	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	San Antonio, TX	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.publication-status	Published	en_US

Abstract:

In this paper, we present the manifold elastic net (MEN) for sparse variable selection. MEN combines merits of the manifold regularization and the elastic net regularization, so it considers both the nonlinear manifold structure of a dataset and the sparse property of the redundant data representation. Face based gender recognition has received much attention in the psychophysical and video surveillance literatures. Most of existing works apply the appearance based information for data representation. A face image with size 40 by 40 could be seen as a point in a linear space with 1600 dimensions. For gender recognition, we have two classes (male and female) in total, so it is essential to find a small number of variables for representation to generalize duly. MEN can duly find the intrinsic structure of a dataset for separating males from the females. Sufficient experimental results on FERET and UMIST datasets suggest that MEN is more effective in selecting discriminative variables for face based gender recognition compared to principal component analysis, sparse principal component analysis, and discriminative locality alignment. ©2009 IEEE.

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