Exploiting side information in locality preserving projection An, Senjian, Liu, Wanquan and Venkatesh, Svetha 2008, Exploiting side information in locality preserving projection, in CVPR 2008 : Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Washington, D. C., pp. 1-8. Attached Files Name Description MIMEType Size Downloads venkatesh-exploitingside-2008.pdf Published version application/pdf 168.04KB 111 Title Exploiting side information in locality preserving projection Author(s) An, Senjian Liu, Wanquan 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 June 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 Computer Society Conference on Computer Vision and Pattern Recognition Start page 1 End page 8 Total pages 8 Publisher IEEE Place of publication Washington, D. C. Keyword(s) access control Australia databases face recognition image retrieval indexing information retrieval kernel linear discriminant analysis scattering Summary Even if the class label information is unknown, side information represents some equivalence constraints between pairs of patterns, indicating whether pairs originate from the same class. Exploiting side information, we develop algorithms to preserve both the intra-class and inter-class local structures. This new type of locality preserving projection (LPP), called LPP with side information (LPPSI), preserves the data's local structure in the sense that the close, similar training patterns will be kept close, whilst the close but dissimilar ones are separated. Our algorithms balance these conflicting requirements, and we further improve this technique using kernel methods. Experiments conducted on popular face databases demonstrate that the proposed algorithm significantly outperforms LPP. Further, we show that the performance of our algorithm with partial side information (that is, using only small amount of pair-wise similarity/dissimilarity information during training) is comparable with that when using full side information. We conclude that exploiting side information by preserving both similar and dissimilar local structures of the data significantly improves performance. 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 1424422426 9781424422425 ISSN 1063-6919 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:30044576 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, 11:33:17 EST Thu, 26 Apr 2012, 17:24:38 EST Thu, 26 Apr 2012, 17:26:55 EST Thu, 03 May 2012, 09:04:56 EST Mon, 04 Aug 2014, 12:40:06 EST Tue, 06 Sep 2016, 18:43:41 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 20 times in Scopus Search Google Scholar Access Statistics: 263 Abstract Views, 111 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:33:17 EST

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