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.
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
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