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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.

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Title Exploiting side information in locality preserving projection
Author(s) An, Senjian
Liu, Wanquan
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 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.
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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
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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.