关键词: 邻域保持嵌入, 线性辨别分析, 流形, 半监督判别邻域嵌入, 人脸识别

Abstract: As a linear version of Locally Linear Embedding（LLE），Neighborhood Preserving Embedding（NPE） can preserve the local geometric structure after embedding and obtain the subspace of the original dataset，hence it has wide practical application in pattern recognition.But it is an unsupervised learning algorithm，which gives rise to some limitations in pattern classification.In this paper，a Semi Supervised Discriminant Neighborhood Embedding（SSDNE） based on NPE is proposed，in which the class information of the labeled samples is considered，also incorporates a regularizer which takes into account of the whole manifold structure.Finally，the algorithm maximizes the ratio of interclass scatter to the intra-class scatter.So the discriminated ability is enhanced and complexity of all labeled data as the case in supervised learning is reduced.Numerical experiments on the ORL and YaleB face databases show that the proposed method outperforms the PCA，LDA，LPP and original NPE algorithm.

Key words: Neighborhood Preserving Embedding（NPE）, linear discriminant analysis, manifold, Semi Supervised Discriminant Neighborhood Embedding（SSDNE）, face recognition