venkatesh-exploitingside-2008.pdf (168.04 kB)
Exploiting side information in locality preserving projection
conference contribution
posted on 2008-01-01, 00:00 authored by S An, W Liu, Svetha VenkateshSvetha VenkateshEven 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
Event
IEEE Conference on Computer Vision and Pattern Recognition (26th : 2008 : Anchorage, Alaska)Pagination
1 - 8Publisher
IEEELocation
Anchorage, AlaskaPlace of publication
Washington, D. C.Start date
2008-06-23End date
2008-06-28ISSN
1063-6919ISBN-13
9781424422425ISBN-10
1424422426Language
engNotes
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E1.1 Full written paper - refereedCopyright notice
2008, IEEETitle of proceedings
CVPR 2008 : Proceedings of the 26th IEEE Conference on Computer Vision and Pattern RecognitionUsage metrics
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