Fuzzy maximal marginal embedding and its application

In this paper, we develops a new approach, called fuzzy maximal marginal embedding (FMME), combining LMME (local maximal marginal embedding) with fuzzy set theory, in which the fuzzy k-nearest neighbor (FKNN) is implemented to achieve the nature distribution information of original samples, and this information is utilized to redefine the affinity weights of neighborhood graph (intraclass and interclass ) instead of the weights of the binary pattern. We can reduce sensitivity of the method to substantial variations between samples caused by varying illumination and shape, viewing conditions. That makes FMME more powerful and robust than other method. The proposed algorithm is examined using Yale and ORL face image databases. The experimental results show FMME outperforms PCA, LDA, LPP and LMME.