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Exploiting side information in locality preserving projection

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conference contribution
posted on 2008-01-01, 00:00 authored by S An, W Liu, Svetha VenkateshSvetha Venkatesh
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.

History

Pagination

1 - 8

Location

Anchorage, Alaska

Open access

  • Yes

Start date

2008-06-23

End date

2008-06-28

ISSN

1063-6919

ISBN-13

9781424422425

ISBN-10

1424422426

Language

eng

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.

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2008, IEEE

Title of proceedings

CVPR 2008 : Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition

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