A Nuclear Norm Based Matrix Regression Based Projections Method for Feature Extraction

Yang, W; Li, J; Zheng, H; Xu, RYD

A Nuclear Norm Based Matrix Regression Based Projections Method for Feature Extraction

Yang, W Li, J Zheng, H Xu, RYD

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Publication Type:: Journal Article
Citation:: IEEE Access, 2017, 6 pp. 7445 - 7451
Issue Date:: 2017-12-16

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Field	Value	Language
dc.contributor.author	Yang, W	en_US
dc.contributor.author	Li, J	en_US
dc.contributor.author	Zheng, H	en_US
dc.contributor.author	Xu, RYD	en_US
dc.date.issued	2017-12-16	en_US
dc.identifier.citation	IEEE Access, 2017, 6 pp. 7445 - 7451	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123298
dc.description.abstract	© 2013 IEEE. In the traditional graph embedding framework, the graph is usually built by k-NN or r-ball. Since it is difficult to manually set the parameters k and r in the high-dimensional space, sparse representation-based methods are usually introduced to automatically build the graphs. In recent years, nuclear norm-based matrix regression (NMR) has been proposed for face recognition using the low rank structural information (i.e., the image matrix-based error model). Inspired by NMR, we give a NMR-based projections (NMRP) method for feature extraction and recognition. The experiments on FERET and extended Yale B face databases show that NMR can be used to build the graph while NMRP is an effective feature extraction method.	en_US
dc.relation.ispartof	IEEE Access	en_US
dc.relation.isbasedon	10.1109/ACCESS.2017.2784800	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Nuclear Norm Based Matrix Regression Based Projections Method for Feature Extraction	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	6	en_US

Abstract:

© 2013 IEEE. In the traditional graph embedding framework, the graph is usually built by k-NN or r-ball. Since it is difficult to manually set the parameters k and r in the high-dimensional space, sparse representation-based methods are usually introduced to automatically build the graphs. In recent years, nuclear norm-based matrix regression (NMR) has been proposed for face recognition using the low rank structural information (i.e., the image matrix-based error model). Inspired by NMR, we give a NMR-based projections (NMRP) method for feature extraction and recognition. The experiments on FERET and extended Yale B face databases show that NMR can be used to build the graph while NMRP is an effective feature extraction method.

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http://hdl.handle.net/10453/123298