A novel approach, namely Fuzzy Discriminant Locality Preserving Projection (FDLPP), is proposed for dimensionality reduction to improve the performance of Discriminant Locality Preserving Projection (DLPP). FDLPP which is based on Maximum Margin Criterion (MMC), pursues to maximize the difference between the locality preserving between-class scatter and locality preserving within-class scatter instead of the ratio. In FDLPP, fuzzy k-nearest is implemented to obtain correct local distribution information and the pursuit of better classification results. Blending the membership degree into the definition of the Laplacian scatter matrix acquire to fuzzy Laplacian scatter matrix. Experiments on ORL, FERET and Yale face databases show the effectiveness with the change in illumination and viewing directions of the proposed method.
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