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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

24

期数:

2014年06期

页码:

114-117

栏目:

智能、算法、系统工程

出版日期:

2014-06-30

文章信息/Info

Title:

Local and Global Weighted Uncorrelated Two-dimensional Discriminant Analysis

文章编号:

1673-629X（2014）06-0114-04

作者:

黄明晓; 荆晓远; 李力; 姚永芳

南京邮电大学 自动化学院

Author(s):

HUANG Ming-xiao; JING Xiao-yuan; LI Li; YAO Yong-fang

关键词:

统计不相关鉴别分析; 鉴别特征; 二维鉴别分析; 二维统计不相关鉴别变换

Keywords:

uncorrelated discriminant analysi; discriminant features; two-dimensional discriminant analysis; two-dimensional uncorrelat-ed discriminant transform

分类号:

TP301

文献标志码:

A

摘要:

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。

Abstract:

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 op-timal discriminant features. In order to address this problem,propose two approaches named Local Two-Dimensional Uncorrelated Dis-criminant 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 experi-mental results on AR and FERET databases demonstrate that the proposed approaches can effectively improve the recognition perform-ance,as compared with some related methods.

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更新日期/Last Update: 1900-01-01