Abstract: To address the limitation that locality preserving projection(LPP) algorithm belongs to unsupervised,a novel approach,named as locality preserving discriminant analysis(LPDA) was proposed. LPDA algorithm absorbs the common characteristics of the manifold learning algorithm and maximum margin criterion(MMC),and can project the high-dimensional face data into the low-dimensional subspace. The new sample can be processed and the small sample size problem can be prevented. Compared with several classical and related methods, the experimental results from Yale, UMIS T and MI T face databases show that I_PDA algorithm can extract the more efficient features for face recognition while the dimensionahty is reduced, and obtains much higher recognition accuracies and stronger power of classification.

Key words: Locality preserving projection, Maximum margin criterion, Feature extraction, Facc recognition, Manifold learning