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. Attached Files Name Description MIMEType Size Downloads venkatesh-boostingperformance-2008.pdf Published version application/pdf 861.03KB 55 Title Boosting performance for 2D linear discriminant analysis via regression Author(s) Nguyen, Nam Liu, Wanquan 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 Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Fri, 20 Apr 2012, 11:33:41 EST Fri, 27 Apr 2012, 10:26:10 EST Fri, 27 Apr 2012, 10:29:02 EST Thu, 03 May 2012, 09:36:51 EST Mon, 04 Aug 2014, 12:44:44 EST Tue, 06 Sep 2016, 18:44:07 EST Tue, 05 Dec 2017, 12:31:25 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 317 Abstract Views, 55 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:33:41 EST

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.