局部和全局加权的二维统计不相关鉴别分析

传统的统计不相关鉴别分析方法使用样本的均值来估计期望,计算出总体散度矩阵。这些方法在数据不满足高斯分布的情况下会出现大的偏差,影响最优鉴别特征的提取。为了解决该问题,文中结合二维鉴别分析的思想,分别提出了基于局部的二维统计不相关鉴别变换（L2DUDT）方法和基于全局加权的二维统计不相关鉴别变换（WG2DUDT）方法。L2DUDT通过用样本的近邻中心来定义每个样本的期望,而WG2DUDT用样本间的欧几里得距离加权来定义期望。基于AR和FERET人脸数据库的实验表明,文中提出的方法与一些相关方法相比,有效地提高了识别性能。

Local and Global Weighted Uncorrelated Two-dimensional Discriminant Analysis

The traditional uncorrelated discriminant analysis methods employ the mean of sample-set to estimate the expectation for all samples,thus computing the total scatter matrix.However,when the data are not Gaussian distributions,these methods may not extract optimal discriminant features.In order to address this problem,propose two approaches named Local Two-Dimensional Uncorrelated Discriminant Transform（ L2DUDT） and Weighted Global Two-Dimensional Uncorrelated Discriminant Transform（ WG2DUDT） on the basis of two-dimensional discriminant analysis respectively.L2DUDT redefines the expectation for each sample using the sample＇s neighbor center,while WG2DUDT uses Euclidean distance between samples as weighted value to construct the expectation.The experimental results on AR and FERET databases demonstrate that the proposed approaches can effectively improve the recognition performance,as compared with some related methods.