关键词: 特别的特征表示, 局部线性K最近邻算法L²KNN, 最近邻集群效应（CENN）, 系数截断方法

Abstract: This paper proposes a novel specific feature representation, then develops a locally linear K-nearest neighbor method called L²KNN accordingly with applications to face recognition. The specific feature representation improves upon the traditional sparse representation by adding up the nonnegative constraint, and the corresponding objective function with the group regularization is optimized to derive a novel approximate specific representation. The proposed method L²KNN shows the clustering effect of nearest neighbors (CENN). It not only can enhance the correlation between the test sample and the training samples with the same label, but also can enhance the correlation among the training samples. The L²KNN method is developed into its classifier L²KNNc (L²KNN-based classifier) in which a coefficients’ truncating method is used to improve the generalization capability of L²KNNc. The experimental results on the face data set indicate the above claim about L²KNNc

Key words: specific feature representation, locally linear K-nearest neighbor method L²KNN, clustering effect of nearest neighbors (CENN), coefficients' truncating method