针对人脸识别问题，提出了一种中心近邻嵌入的学习算法，其与经典的局部线性嵌入和保局映射不同，它是一种有监督的线性降维方法。该方法首先通过计算各类样本中心，并引入中心近邻距离代替两样本点之间的直接距离作为权系数函数的输入；然后再保持中心近邻的几何结构不变的情况下把高维数据嵌入到低维坐标系中。通过中心近邻嵌入学习算法与其他3种人脸识别方法（即主成分分析、线形判别分析及保局映射）在ORL、Yale及UMIST人脸库上进行的比较实验结果表明，它在高维数据低维可视化和人脸识别效果等方面均较其他3种方法取得了更好的效果。

In this paper，a novel learning algorithm called center based neighborhood embedding(CNE) is proposed to deal with face recognition. Unlike the classical methods such as local linear embedding(LLE) and local preserving projection(LPP)，CNE is a supervised linear dimensionality reduction method. It first computes centers of all sample classes. The input of the weight function between two samples was replaced by center based neighborhood(CN) distance. Then，the high dimensional data are embedded into a low dimensional space with preserving the CN geometric structure. The CNE approach is compared with principle component analysis(PCA)，linear discriminant analysis(LDA) and local preserving projection(LPP) on ORL，Yale and UMIST databases. Experiments demonstrate the proposed method is superior to other three methods in terms of both lower dimensional visualization and recognition accuracy.