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. Attached Files Name Description MIMEType Size Downloads venkatesh-afastkernel-2005.pdf Published version application/pdf 4.80MB 24 Title A fast kernel dimension reduction algorithm with applications to face recognition Author(s) An, Senjian Liu, Wanquan 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 Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges   Connect to conference webpage 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, 11:35:32 EST Fri, 27 Apr 2012, 16:24:32 EST Fri, 27 Apr 2012, 16:26:04 EST Thu, 03 May 2012, 11:58:43 EST Thu, 03 May 2012, 12:01:36 EST Mon, 15 Sep 2014, 15:35:39 EST Tue, 06 Sep 2016, 18:46:00 EST Sat, 21 Oct 2017, 02:00:57 EST Tue, 05 Dec 2017, 12:31:33 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 319 Abstract Views, 24 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:35:32 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.