中文摘要：

针对流形学习在人脸识别中的应用，该文提出基于局部保持投影（Locality Preserving Projection，LPP）的监督线性维数约简方法。利用样本的类别信息，将LPP的最近邻图分解为类内图和类外图，通过优化，最优保持同类数据固有的局部邻域关系，缩小数据之间的距离，同时最大化不同类数据之间的距离，从而增大各类数据分布之间的间隔，提高了嵌入空间的辨别能力。此外，在构建图的过程中采用了自适应邻域，增强了对数据分布稀疏性的表征。在ExtendedYaleB和CMuPIE两个开放人脸数据库上进行了试验，验证了算法的有效性。

英文摘要：

A novel supervised linear method based on Locality Preserving Projection （LPP） of reducing dimensionality is proposed for face recognition. In this study, the nearest neighbor graph of LPP is split into within-class graph and between-class graph according to the class label information of samples. After optimizing, the intrinsic local neighbor structure of the samples of same class is maintained and the distances between them are decreased. Meanwhile, the distances between the samples of different class are maximized to increase the space of the distribution of all kinds of samples, and thus the discriminability of the embedding is enhanced. In addition, adaptive neighborhood is applied to the construction of the graph, with the characterization for the sparsity of the sample improved. Experimental results on the two open face databases, Extended Yale B and CMU PIE face database, show that the proposed algorithm improves the accuracy of face recogntion effectively.