An Improved Kernel Minimum Square Error Classification Algorithm Based on L<inf>2,1</inf>-Norm Regularization

Liu, Z; Xue, S; Zhang, L; Pu, J; Wang, H

An Improved Kernel Minimum Square Error Classification Algorithm Based on L<inf>2,1</inf>-Norm Regularization

Liu, Z Xue, S

Zhang, L Pu, J Wang, H

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Publication Type:: Journal Article
Citation:: IEEE Access, 2017, 5 pp. 14133 - 14140
Issue Date:: 2017-07-21

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Field	Value	Language
dc.contributor.author	Liu, Z	en_US
dc.contributor.author	Xue, S https://orcid.org/0000-0002-9123-5133	en_US
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Pu, J	en_US
dc.contributor.author	Wang, H	en_US
dc.date.issued	2017-07-21	en_US
dc.identifier.citation	IEEE Access, 2017, 5 pp. 14133 - 14140	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127230
dc.description.abstract	© 2013 IEEE. The kernel minimum square error classification (KMSEC) algorithm has been widely used in classification problems. It shows a good performance on image data besides the following drawbacks: Not sparse in the solutions and sensitive to noises. The latter drawback will result in a decrease in the recognition performance. To this end, we propose an improved (IKMSEC) by using the L2,1-norm regularization, which can obtain a sparse representation of nonlinear features to guarantee an efficient classification performance. The comprehensive experiments show the promising results in face recognition and image classification.	en_US
dc.relation.ispartof	IEEE Access	en_US
dc.relation.isbasedon	10.1109/ACCESS.2017.2730218	en_US
dc.title	An Improved Kernel Minimum Square Error Classification Algorithm Based on L<inf>2,1</inf>-Norm Regularization	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.for	0804 Data Format	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/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	5	en_US

Abstract:

© 2013 IEEE. The kernel minimum square error classification (KMSEC) algorithm has been widely used in classification problems. It shows a good performance on image data besides the following drawbacks: Not sparse in the solutions and sensitive to noises. The latter drawback will result in a decrease in the recognition performance. To this end, we propose an improved (IKMSEC) by using the L2,1-norm regularization, which can obtain a sparse representation of nonlinear features to guarantee an efficient classification performance. The comprehensive experiments show the promising results in face recognition and image classification.

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http://hdl.handle.net/10453/127230