关键词: 无监督判别投影, 非参数鉴别分析, 图像欧式距离, 流形学习, 半监督学习, 人脸识别

Abstract: Aiming at the limitation of ignoring the local structure feature of the traditional linear dimensionality reduction methods，a new semi-supervised manifold learning is proposed.On the basis of the character of the face image，this method gets K-nearest neighbors of each sample by calculating the image euclidean distance，and the adjacency matrix of unsupervised discriminant projection is modified accordingly.Finally，the proposed method that combines labeled samples with modified unsupervised discriminant projection is presented to achieve optimal geometric projection.Extensive experimental results on several public face databases validate the correctness and effectiveness of the proposed approach.

Key words: unsupervised discriminant projection, nonparametric discriminant analysis, image euclidean distance, manifold learning, semi-supervised learning, face recognition