Abstract:

Locally linear discriminant embedding (LLDE) can effectively enhance the discriminability of the locally linear embedding (LLE) by adding the criterion of maximum margin criterion (MMC) into the objective function of LLE. However, LLDE seeks to preserve the global discriminative information of the sample and the optimal result only achieved when the data is of Gaussian distribution. A novel supervised dimensionality reduction method, namely nonparametric locally linear discriminant embedding (NLLDE), was proposed by adding the criterion of weighted nonparametric maximum margin criterion (WNMMC) into the objective function of LLE to overcome the drawbacks of LLDE. NLLDE explored the local discriminative information of the data, which had more discriminating power. Furthermore, NLLDE did not assume the particular form of class densities. This made NLDE could be applied in more fields. The experimental results on Yale and PIE face database indicated the effectivity of this method.

Key words: dimensionality reduction;subspace learning;face recognition, linear embedding