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Boosting performance for 2D linear discriminant analysis via regression

Nguyen, Nam, Liu, Wanquan and Venkatesh, Svetha 2008, Boosting performance for 2D linear discriminant analysis via regression, in ICPR 2008 : Proceedings of the 19th International Conference on Pattern Recognition, IEEE, Washington, D. C., pp. 1-4, doi: 10.1109/ICPR.2008.4761898.

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Title Boosting performance for 2D linear discriminant analysis via regression
Author(s) Nguyen, Nam
Liu, Wanquan
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name International Conference on Pattern Recognition (19th : 2008 : Tampa, Fla.)
Conference location Tampa, Fla.
Conference dates 8-11 Dec. 2008
Title of proceedings ICPR 2008 : Proceedings of the 19th International Conference on Pattern Recognition
Editor(s) [Unknown]
Publication date 2008
Conference series International Conference on Pattern Recognition
Start page 1
End page 4
Total pages 4
Publisher IEEE
Place of publication Washington, D. C.
Keyword(s) boosting
computational efficiency
covariance matrix
face recognition
image databases
linear discriminant analysis
principal component analysis
strontium
vectors
Summary Two Dimensional Linear Discriminant Analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called Regularized 2DLDA and Ridge Regression for 2DLDA (RR-2DLDA). Regularized 2DLDA is an extension of 2DLDA with the introduction of a regularization parameter to deal with the small sample size problem. RR-2DLDA integrates ridge regression into Regularized 2DLDA to balance the distances among different classes after the transformation. These proposed algorithms overcome the limitations of 2DLDA and boost recognition accuracy. The experimental results on the Yale, PIE and FERET databases showed that RR-2DLDA is superior not only to 2DLDA but also other state-of-the-art algorithms.
ISBN 1424421748
9781424421749
ISSN 1051-4651
Language eng
DOI 10.1109/ICPR.2008.4761898
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2008, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044583

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.