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A fast kernel dimension reduction algorithm with applications to face recognition

An, Senjian, Liu, Wanquan, Venkatesh, Svetha and Tjahyadi, Ronny 2005, A fast kernel dimension reduction algorithm with applications to face recognition, in ICMLC 2005 : Proceedings of the 4th International Conference on Machine Learning and Cybernetics, IEEE, [Washington, D. C.], pp. 3369-3376, doi: 10.1109/ICMLC.2005.1527524.

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Title A fast kernel dimension reduction algorithm with applications to face recognition
Author(s) An, Senjian
Liu, Wanquan
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Tjahyadi, Ronny
Conference name International Conference on Machine Learning and Cybernetics (4th : 2005 : Guangzhou, China)
Conference location Guangzhou, China
Conference dates 18-21 Aug. 2005
Title of proceedings ICMLC 2005 : Proceedings of the 4th International Conference on Machine Learning and Cybernetics
Editor(s) [Unknown]
Publication date 2005
Conference series International Conference on Machine Learning and Cybernetics
Start page 3369
End page 3376
Total pages 8
Publisher IEEE
Place of publication [Washington, D. C.]
Keyword(s) classification
dimensional Reduction
face Recognition
optimization
support vector machine
Summary This paper presents a novel dimensionality reduction algorithm for kernel based classification. In the feature space, the proposed algorithm maximizes the ratio of the squared between-class distance and the sum of the within-class variances of the training samples for a given reduced dimension. This algorithm has lower complexity than the recently reported kernel dimension reduction(KDR) for supervised learning. We conducted several simulations with large training datasets, which demonstrate that the proposed algorithm has similar performance or is marginally better compared with KDR whilst having the advantage of computational efficiency. Further, we applied the proposed dimension reduction algorithm to face recognition in which the number of training samples is very small. This proposed face recognition approach based on the new algorithm outperforms the eigenface approach based on the principle component analysis (PCA), when the training data is complete, that is, representative of the whole dataset.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 0780390911
Language eng
DOI 10.1109/ICMLC.2005.1527524
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 ©2005, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044615

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.