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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...
Face images are always affected by variations in illumination conditions and different facial expressions in the real world. Recently, local discriminant embedding (LDE) was proposed to manifold learning and pattern classification. LDE achieves good discriminating performance by integrating the information of neighbor and class relations between data points. But LDE cannot solve illumination problem...
In two-dimensional local graph embedding discriminant analysis(2DLGEDA), the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring within the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. But in the real world, face images are always affected by variations in illumination conditions...
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