Exponential locality preserving projections for small sample size problem

Authors:
Su-Jing Wang;Hui-Ling Chen;Xu-Jun Peng;Chun-Guang Zhou
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun 130012, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China;Raytheon BBN Technologies, Boston, MA 02138, USA;College of Computer Science and Technology, Jilin University, Changchun 130012, China
Venue:
Neurocomputing
Year:
2011

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Abstract

Locality preserving projections (LPP) is a widely used manifold reduced dimensionality technique. However, it suffers from two problems: (1) small sample size problem and (2) the performance is sensitive to the neighborhood size k. In order to address these problems, we propose an exponential locality preserving projections (ELPP) by introducing the matrix exponential in this paper. ELPP avoids the singular of the matrices and obtains more valuable information for LPP. The experiments are conducted on three public face databases, ORL, Yale and Georgia Tech. The results show that the performances of ELPP is better than those of LPP and the state-of-the-art LPP Improved1.