Abstract Neighborhood preserving embedding directly reconstructs the sample by its K-nearest neighbors. However, it does not distinguish the importance between intra-class neighbors and inter-class neighbors, which leads to poor recognition performance. In this paper, a common vector-based fuzzy neighborhood preserving embedding (FNPE/CV) algorithm is proposed.Firstly, the degree of membership of every sample for each class is obtained based on the class labels of its K-nearest neighbors. Then, every sample is reconstructed by the common vector and its membership grade for every class. Finally, the problem of minimizing the residual between original sample and its reconstruction sample is converted to solve the generalized eigenvalue problem to obtain the final projection transformation matrix. After the projecting, FNPE/CV minimizes the difference among intra-class samples and separates inter-class samples as far as possible. The experiments on ORL, Yale, AR and PIEC29 face databases demonstrate the effectiveness of the proposed algorithm.
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2014, Vol. 27 
