Laplacian Discriminant Projection with Optimized Kernels for Supervised Feature Extraction and Classification

A novel feature extraction method, namely Laplacian discriminant projection with optimized kernels (KLDP-Opt) algorithm is proposed in this paper. The advantage of KLDP-Opt lies in: 1) the similarity matrix is constructed with the class-wise nonparametric similarity measure where it solves procedure selection problem; 2) data-dependent kernel is applied to solve the limitation of linearity of LPP, where the adaptive parameters of the data-dependent kernel are computed through optimizing an objective function designed for measuring the class separability of data in the feature space. Besides the theory derivation, the experiments are implemented on ORL and Yale face databases to evaluate the feasibility of the proposed algorithm.