新的局部保持鉴别分析算法

Novel local preserving based discriminant analysis algorithm

提出了一种新的局部保持鉴别分析算法:基于迹比准则与自适应近邻图嵌入的局部保持鉴别分析算法。根据样本分布特性自适应构建类内和类间近邻图,保持数据的局部结构并且利用数据的鉴别信息,定义局部类内离差矩阵以及局部类间离差矩阵,采用迹比Fisher判别函数作为目标函数,通过迭代的方法最大化局部类间离差矩阵与类内离差矩阵的迹比值,解得最优子空间。在ORL和Yale人脸数据库上的实验表明该方法是有效的。

In this paper, a novel local preserving based discriminant analysis algorithm, trace ratio based local discriminant analysis algorithm using adaptive neighborhood graph, is proposed. To implement the algorithm, it adaptively constructs with-in-class and between neighborhood graph according to samples distribution, preserves local structure of the data manifold and utilizes its discriminant information to define local within-class scatter matrix and local between-class scatter matrix, ultimately gains optimal subspace by iteratively maximizing the trace ratio of local within-class scatter matrix and local between-class scatter matrix. The experiments on ORL and Yale face database demonstrate the effectiveness of the proposed algorithm.