Article: Regularized orthogonal linear discriminant analysis

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TitleRegularized orthogonal linear discriminant analysis
AuthorsChing, WK1
Chu, D3
Liao, LZ2
Wang, X3
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
CitationPattern Recognition, 2012, v. 45 n. 7, p. 2719-2732 [How to Cite?]
DOI: http://dx.doi.org/10.1016/j.patcog.2012.01.007
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.
ISSN0031-3203
2011 Impact Factor: 2.292
2011 SCImago Journal Rankings: 0.119
DOIhttp://dx.doi.org/10.1016/j.patcog.2012.01.007
ISI Accession Number IDWOS:000302451000022
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.

ReferencesReferences in Scopus
DC Field
Value
dc.contributor.authorChing, WK
dc.contributor.authorChu, D
dc.contributor.authorLiao, LZ
dc.contributor.authorWang, X
dc.date.accessioned2012-03-27T09:00:58Z
dc.date.available2012-03-27T09:00:58Z
dc.date.issued2012
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.
dc.description.natureLink_to_subscribed_fulltext
dc.identifier.citationPattern Recognition, 2012, v. 45 n. 7, p. 2719-2732 [How to Cite?]
DOI: http://dx.doi.org/10.1016/j.patcog.2012.01.007
dc.identifier.citeulike10296103
dc.identifier.doihttp://dx.doi.org/10.1016/j.patcog.2012.01.007
dc.identifier.epage2732
dc.identifier.hkuros198996
dc.identifier.isiWOS:000302451000022
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.

dc.identifier.issn0031-3203
2011 Impact Factor: 2.292
2011 SCImago Journal Rankings: 0.119
dc.identifier.issue7
dc.identifier.scopuseid_2-s2.0-84862798516
dc.identifier.spage2719
dc.identifier.urihttp://hdl.handle.net/10722/145892
dc.identifier.volume45
dc.languageeng
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pr
dc.publisher.placeNetherlands
dc.relation.ispartofPattern Recognition
dc.relation.referencesReferences in Scopus
dc.subjectData dimensionality reduction
dc.subjectOrthogonal linear discriminant analysis
dc.subjectQR factorization
dc.subjectRegularized orthogonal linear discriminant analysis
dc.titleRegularized orthogonal linear discriminant analysis
dc.typeArticle
Author Affiliations
  1. The University of Hong Kong
  2. Hong Kong Baptist University
  3. National University of Singapore