基于局部均值的边界鉴别分析方法

Marginal discriminant analysis based on local mean

提出一种基于局部均值的监督降维算法.找出与每一个样本点同类最远的k1个近邻的均值和异类最近的k2个近邻的均值,构造反映类内散布和类间散布的矩阵,由特征值分解确定特征提取变换.该方法使不同类别样本点之间的边界在投影子空间尽可能扩大,保留了数据的邻域结构,具有较强的模式可分离性.通过在ORL和YALE两个标准人脸数据库上与其他降维算法的对比识别实验,证实了算法的有效性.

This paper develops a supervised dimensionality reduction method based on local mean（LMMDA）.For every point,LMMDA tries to find the mean of the farthest k1-neighbor points with the same class label and the mean of the nearest k2-neighbor points with the different class label,constructs within-class scatter matrix and between-class scatter matrix,and extracts features through the fisher criterion.This method enlarges the margin of the different class data point.It can retain the true neighborhood structure of the data,and has strong robustness.The experimental results on ORL and Yale face image database show the effectiveness of proposed method.