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Ridge regression for two dimensional locality preserving projection

Nguyen, Nam, Liu, Wanquan and Venkatesh, Svetha 2008, Ridge regression for two dimensional locality preserving projection, in ICPR 2008 : Proceedings of the 19th International Conference on Pattern Recognition, IEEE, Washington, D. C., pp. 1-4, doi: 10.1109/ICPR.2008.4761132.

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Title Ridge regression for two dimensional locality preserving projection
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) computational costs
face data
face recognition algorithms
FERET database
locality preserving projections
novel algorithm
ridge regression
Summary Two Dimensional Locality Preserving Projection (2D-LPP) is a recent extension of LPP, a popular face recognition algorithm. It has been shown that 2D-LPP performs better than PCA, 2D-PCA and LPP. However, the computational cost of 2D-LPP is high. This paper proposes a novel algorithm called Ridge Regression for Two Dimensional Locality Preserving Projection (RR- 2DLPP), which is an extension of 2D-LPP with the use of ridge regression. RR-2DLPP is comparable to 2DLPP in performance whilst having a lower computational cost. The experimental results on three benchmark face data sets - the ORL, Yale and FERET databases - demonstrate the effectiveness and efficiency of RR-2DLPP compared with other face recognition algorithms such as PCA, LPP, SR, 2D-PCA and 2D-LPP.
ISBN 1424421748
9781424421749
ISSN 1051-4651
Language eng
DOI 10.1109/ICPR.2008.4761132
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:30044589

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.