为了取得更好的识别效果,受支持向量机的几何解释和最近点问题启发，提出了一种新的模式分类算法——仿射子空间最近点算法。该算法是将支持向量机最近点法的最近点搜索区域由两类训练集凸包推广到两类训练样本各自张成的仿射子空间，并以仿射子空间作为样本分布的粗略估计，通过仿射子空间中的最近点对来构造平分仿射子空间间隔的最优分类超平面。该算法在ORL人脸识别数据库上进行的比较实验中取得了较好的识别效果，从而证实了该方法的可行性和有效性。

A novel pattern classification algorithm called Affine subspace Nearest Points (ASNP) algorithm is presented in this paper. Inspired by the geometrical explanation of Support Vector Machine (SVM) and the nearest point method, in which the optimal separating plane bisects the closest points within two convex hulls, the ASNP algorithm expands the searching areas of the closest points from the convex hulls to their corresponding class affine subspaces. The affine subspaces are taken as the rough estimations of the class sample distributions, and their closest points are found. Then, the hyperplane to separate the affine subspaces with the maximal margins is constructed, which is the perpendicular bisector of the line segment joining the two closest points. The test experiments compared with the Nearest Neighbor (1 NN) classifier and SVM on the ORL face recognition database showed good performance of this algorithm.