完备自适应近邻图嵌入的局部鉴别投影算法

针对基于自适应近邻图嵌入的局部鉴别投影算法（Neighborhood graph embedding based local adaptive discriminant analysis, LADP ）仅仅利用局部类内离差矩阵主元空间的鉴别信息而丢失了其零空间内大量鉴别信息的不足，结合全空间的基本思想提出了完备的基于自适应近邻图嵌入的局部鉴别投影算法（ Complete LADP,CLADP）。在局部类内离差矩阵的零空间内，通过最大化局部类间离差矩阵提取不规则鉴别特征，在局部类间离差矩阵的主元空间内，通过最大化局部类间离差矩阵的同时最小化局部类 内离差矩阵提取规则鉴别特征，最后将不规则鉴别特征和规则鉴别特征串联形成CLADP特征。在ORL，Yale以及PIE人脸库上的人脸识别实验结果证明了CLADP的有效性。

Local Discriminant Projection Algrorithm Based on Complete Adaptive Neighborhood Graph Embedding

The existing adaptive neighborhood graph embedding method based on local discriminant projection(LADP) only uses discriminant information in the principle space of local within class scatter matrix, which leads to the loss of discriminant information in the null space. To overcome the drawback of LADP, a complete LADP(CLADP) is proposed for face recognition. In the null space of local within class scatter matrix, irregular discriminant features are extracted by maximizing the local between class scatter matrix. In the principle space of local within class scatter matrix, regular discriminant features are extracted by maximizing the local between class scatter matrix and minimizing the local within class scatter matrix. Finally, irregular discriminant features and regular discriminant features are combined as the features of CLADP for face recognition. The experimental results on ORL, Yale face database and PIE subset illustrate the effectiveness of the proposed CLADP.