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Robust learning of discriminative projection for multicategory classification on the Stiefel manifold

Pham, Duc-Son and Venkatesh, Svetha 2008, Robust learning of discriminative projection for multicategory classification on the Stiefel manifold, in CVPR 2008 : Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Washington, D. C., pp. 1-7.

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Title Robust learning of discriminative projection for multicategory classification on the Stiefel manifold
Author(s) Pham, Duc-Son
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name IEEE Conference on Computer Vision and Pattern Recognition (26th : 2008 : Anchorage, Alaska)
Conference location Anchorage, Alaska
Conference dates 23-28 Jun. 2008
Title of proceedings CVPR 2008 : Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition
Editor(s) [Unknown]
Publication date 2008
Conference series IEEE Conference on Computer Vision and Pattern Recognition
Start page 1
End page 7
Total pages 7
Publisher IEEE
Place of publication Washington, D. C.
Keyword(s) Australia
databases
face recognition
lighting
linear discriminant analysis
nearest neighbor searches
pattern recognition
principal component analysis
robustness
statistical learning
Summary Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in pose, illumination, and facial expression. To address this problem, we propose a framework formulated under statistical learning theory that facilitates robust learning of a discriminative projection. Dimensionality reduction using the projection matrix is combined with a linear classifier in the regularized framework of lasso regression. The projection matrix in conjunction with the classifier parameters are then found by solving an optimization problem over the Stiefel manifold. The experimental results on standard face databases suggest that the proposed method outperforms some recent regularized techniques when the number of training samples is small.
ISBN 1424422426
9781424422425
Language eng
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
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044580

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.