A maximum margin unsupervised orthogonal feature extraction algorithm based on principal component analysis and locality preserving projection is proposed

in which both global and local features of the data samples are taken into account.The proposed method adopts the maximum margin criterion as object function and avoids the small sample size problem.To further enhance the recognition performance of the algorithm

orthogonal constraint projection matrix is given.Experimental results on face database demonstrate the effectiveness of the proposed method.