中文摘要：

提出一种近邻类鉴别分析方法，线性鉴别分析是该方法的一个特例．线性鉴别分析通过最大化类间散度同时最小化类内散度寻找最佳投影，其中类间散度是所有类之间散度的总体平均；而近邻类鉴别分析中类间散度定义为各个类与其％个近邻类之间的平均散度．该方法通过选取适当的近邻类数，能够缓解线性鉴别降维后造成的部分类的重叠．实验结果表明近邻类鉴别分析方法性能稳定且优于传统的线性鉴别分析．

英文摘要：

A method of neighbor class linear discriminant analysis （NCLDA） is proposed. Linear discriminant analysis （LDA） is a special case of this method. LDA finds the optimal projections by maximum between-class scatter while by minimum within-class scatter. The between-class scatter is an average over divergences among all classes. In NCLDA, between-class scatter is defined as average divergences between one class and its k nearest neighbor classes. By selecting proper numbers of neighbor class, NCLDA alleviates overlaps among classes caused by LDA. The experimental results show that the proposed NCLDA is robust and outperforms LDA.