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
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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