Exploiting side information in locality preserving projection

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 comp...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Senjian An, Wanquan Liu, Svetha Venkatesh
• Keywords: Class Label Information, Computer Vision, CVPR 2008, Dissimilar Local Structures, Locality Preserving Projection, Pair-wise Similarity/dissimilarity Information, Similar Training Patterns