Exponential locality preserving projections for small sample size problem |
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Authors: | Su-Jing WangAuthor VitaeHui-Ling ChenAuthor Vitae Xu-Jun PengAuthor VitaeChun-Guang ZhouAuthor Vitae |
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Affiliation: | a College of Computer Science and Technology, Jilin University, Changchun 130012, China b Raytheon BBN Technologies, Boston, MA 02138, USA |
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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. |
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Keywords: | Locality preserving projections Small sample size problem Matrix exponential Facial recognition |
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