相异度导引的有监督鉴别分析方法  

Supervised discriminant analysis derived from dissimilarity

作　　者：俞振洲[1] 王正群[1] 陈广花[1] 

机构地区：[1]扬州大学信息工程学院,江苏扬州225009

出　　处：《计算机工程与应用》2011年第23期208-211,共4页Computer Engineering and Applications

基　　金：国家自然科学基金No.60875004;No.60774017;江苏省自然科学基金(No.BK2009184);江苏省高校自然科学基础研究资助项目(No.07KJB520133)~~

摘　　要：提出了相异度导引的有监督鉴别分析方法(D-SDA)。结合模式局部信息和全局信息,定义了类内散度权重矩阵R^W和类间散度权重矩阵R^B,分别表示类内样本的相异度、类间样本的相异度。由R^W、R^B导出类内散度矩阵S^W和类间散度矩阵S^B,根据Fisher鉴别准则函数确定最优变换矩阵。在YALE和AR人脸图像库上的实验验证了这一算法的有效性。This paper proposes a novel supervised discriminant analysis method derived from dissimilarity.Combining pattern local and global information,the new within-class and between-class scatter weight matrixes are defined.They represent dissimilarity of within-class sample and between-class sample respectively.The new within-class scatter and between-class scatter matrix are derived from the new scatter weight matrixes.The optimal transformation matrix is determined according to the Fisher criterion.The experimental results on YALE and AR face image database show that the proposed method outperforms the traditional approaches.

关 键 词：散度权重矩阵 FISHER判别准则 相异度 全局与局部信息 人脸识别 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]