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Article: Regularized orthogonal linear discriminant analysis

TitleRegularized orthogonal linear discriminant analysis
Authors
KeywordsData dimensionality reduction
Orthogonal linear discriminant analysis
QR factorization
Regularized orthogonal linear discriminant analysis
Issue Date2012
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pr
Citation
Pattern Recognition, 2012, v. 45 n. 7, p. 2719-2732 How to Cite?
AbstractIn this paper the regularized orthogonal linear discriminant analysis (ROLDA) is studied. The major issue of the regularized linear discriminant analysis is to choose an appropriate regularization parameter. In existing regularized linear discriminant analysis methods, they all select the best regularization parameter from a given parameter candidate set by using cross-validation for classification. An obvious limitation of such regularized linear discriminant analysis methods is that it is not clear how to choose an appropriate candidate set. Therefore, up to now, there is no concrete mathematical theory available in selecting an appropriate regularization parameter in practical applications of the regularized linear discriminant analysis. The present work is to fill this gap. Here we derive the mathematical relationship between orthogonal linear discriminant analysis and the regularized orthogonal linear discriminant analysis first, and then by means of this relationship we find a mathematical criterion for selecting the regularization parameter in ROLDA and consequently we develop a new regularized orthogonal linear discriminant analysis method, in which no candidate set of regularization parameter is needed. The effectiveness of our proposed regularized orthogonal linear discriminant analysis is illustrated by some real-world data sets. © 2012 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/145892
ISSN
2014 Impact Factor: 3.096
2014 SCImago Journal Rankings: 2.477
ISI Accession Number ID
Funding AgencyGrant Number
University of Hong Kong
Research Grant Council of Hong Kong
NUSR-146-000-140-112
GRF from Research Grant Council of Hong KongHKBU201409
HKBU201611
Funding Information:

This author was supported in part by grants from The University of Hong Kong, and the Research Grant Council of Hong Kong.

References

 

DC FieldValueLanguage
dc.contributor.authorChing, WKen_HK
dc.contributor.authorChu, Den_HK
dc.contributor.authorLiao, LZen_HK
dc.contributor.authorWang, Xen_HK
dc.date.accessioned2012-03-27T09:00:58Z-
dc.date.available2012-03-27T09:00:58Z-
dc.date.issued2012en_HK
dc.identifier.citationPattern Recognition, 2012, v. 45 n. 7, p. 2719-2732en_HK
dc.identifier.issn0031-3203en_HK
dc.identifier.urihttp://hdl.handle.net/10722/145892-
dc.description.abstractIn this paper the regularized orthogonal linear discriminant analysis (ROLDA) is studied. The major issue of the regularized linear discriminant analysis is to choose an appropriate regularization parameter. In existing regularized linear discriminant analysis methods, they all select the best regularization parameter from a given parameter candidate set by using cross-validation for classification. An obvious limitation of such regularized linear discriminant analysis methods is that it is not clear how to choose an appropriate candidate set. Therefore, up to now, there is no concrete mathematical theory available in selecting an appropriate regularization parameter in practical applications of the regularized linear discriminant analysis. The present work is to fill this gap. Here we derive the mathematical relationship between orthogonal linear discriminant analysis and the regularized orthogonal linear discriminant analysis first, and then by means of this relationship we find a mathematical criterion for selecting the regularization parameter in ROLDA and consequently we develop a new regularized orthogonal linear discriminant analysis method, in which no candidate set of regularization parameter is needed. The effectiveness of our proposed regularized orthogonal linear discriminant analysis is illustrated by some real-world data sets. © 2012 Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pren_HK
dc.relation.ispartofPattern Recognitionen_HK
dc.subjectData dimensionality reductionen_HK
dc.subjectOrthogonal linear discriminant analysisen_HK
dc.subjectQR factorizationen_HK
dc.subjectRegularized orthogonal linear discriminant analysisen_HK
dc.titleRegularized orthogonal linear discriminant analysisen_HK
dc.typeArticleen_HK
dc.identifier.emailChing, WK:wching@hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.patcog.2012.01.007en_HK
dc.identifier.scopuseid_2-s2.0-84862798516-
dc.identifier.hkuros198996en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84857995419&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume45en_HK
dc.identifier.issue7en_HK
dc.identifier.spage2719en_HK
dc.identifier.epage2732en_HK
dc.identifier.isiWOS:000302451000022-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridChu, D=7201734138en_HK
dc.identifier.scopusauthoridLiao, LZ=26642961500en_HK
dc.identifier.scopusauthoridWang, X=54942097300en_HK
dc.identifier.citeulike10296103-

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