Abstract
Locality Preserving Projections (LPP) is a linear projective map that arises by solving a variational problem that optimally preserves the neighborhood structure of the data set. Though LPP has been applied in many domains, it has limits to solve recognition problem. Thus, Discriminant Locality Preserving Projections (DLPP) is presented in this paper. The improvement of DLPP algorithm over LPP method benefits mostly from two aspects. One aspect is that DLPP tries to find the subspace that best discriminates different face classes by maximizing the between-class distance, while minimizing the within-class distance. The other aspect is that DLPP reduces the energy of noise and transformation difference as much as possible without sacrificing much of intrinsic difference. In the experiments, DLPP achieves the better face recognition performance than LPP.
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