UM
A least-squares model to orthogonal linear discriminant analysis
Zhang T.; Fang B.; Tang Y.Y.; Shang Z.; He G.
2010-06-01
Source PublicationInternational Journal of Pattern Recognition and Artificial Intelligence
ISSN02180014
Volume24Issue:4Pages:635-650
AbstractOrthogonal transformation can delete the correlations among candidate features such that the extracted features do not disturb each other. An orthogonal set of discriminant vectors is more powerful than the classical discriminant vectors. In this paper, we present a new orthogonal linear discriminant analysis (OLDA) model based on least-squares approximation called LS-OLDA for pattern classification, which aims to find an orthogonal transformation W and a diagonal matrix D such that the difference between S S and WDW is minimized in the least-squares sense, and the trace of D is maximized simultaneously. Theoretical analysis shows that the proposed model coincides with classical OLDA criterion. The experimental results on different standard data sets compared with related methods show that LS-OLDA achieves or approximates closely to the best accuracy, and has lower computational cost. © 2010 World Scientific Publishing Company.
KeywordLeast-squares model matrix diagonalization orthogonal linear discriminant analysis
DOI10.1142/S0218001410008068
URLView the original
Language英語
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
AffiliationChongqing University
Recommended Citation
GB/T 7714
Zhang T.,Fang B.,Tang Y.Y.,et al. A least-squares model to orthogonal linear discriminant analysis[J]. International Journal of Pattern Recognition and Artificial Intelligence,2010,24(4):635-650.
APA Zhang T.,Fang B.,Tang Y.Y.,Shang Z.,&He G..(2010).A least-squares model to orthogonal linear discriminant analysis.International Journal of Pattern Recognition and Artificial Intelligence,24(4),635-650.
MLA Zhang T.,et al."A least-squares model to orthogonal linear discriminant analysis".International Journal of Pattern Recognition and Artificial Intelligence 24.4(2010):635-650.
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