关键词: k近邻, 局部保持投影, 自适应邻域, 样本列, 结构特征

Abstract: Through introducing the column information of sample, this paper proposes an improved locality preserving projection (LPP) algorithm named adaptive neighbor and corresponding columns of the samples based graph construction method on LPP (ANCCG-LPP) to overcome the defects which parameter k is difficultly selected in traditional k-nearest neighbor graph and the original structure of the image sample is easily ignored by one dimensional vector of the sample for LPP. In the proposed algorithm, corresponding column neighbors of column samples are   determined adaptively by the column information of the samples, and then the neighbors of the sample are determined adaptively by the number of coupled column neighbors between two samples. Finally, the optimal projection vectors are solved by redefining the weight matrix to optimize the objective function. Supervised ANCCG-LPP algorithm based on ANCCG-LPP is put forward through adding class information of the samples. The simulation experiments on ORL, Yale Extended B face databases validate the effectiveness of the ANCCG-LPP and SANCCG-LPP.

Key words:  k-nearest neighbor, locality preserving projection, adaptive neighborhood, sample column, structural characteristics