现代电子技术Issue(22)：24-27,4.
基于无穷范数的二值线性判别分析
Binary linear discriminant analysis based on infinity norm
摘要
Abstract
The linear discriminant analysis(LDA)is a method of supervised feature extraction. It has been widely used in the field of computer vision such as face recognition. An infinite norm based LDA method is proposed this paper to improve the efficiency of feature extraction. Traditional LDA methods express their objective functions as either difference of between-class scattering matrix and within-class scattering matrix or quotient in the L2 norm. Consequently,these methods need to involve in matrix inversion and eigen-value decomposition. By contrast,the proposed method utilizes L-norm(infinite norm)instead of L2 norm to formulate the objective function with respect to the difference between between-class scatter matrix and within-class scat-ter matrix. Because the solution is obtained iteratively,this method avoids time-consuming eigen-decomposition. Moreover,the projection vector realizes binarization,and the value of elements is -1 or 1,resulting in high efficiency because it avoids compu-ting the inner product between a sample and the projection vector. The results of experiments in ORL database and Yale data-base demonstrate the efficiency and effectiveness of the proposed method.关键词
线性判别分析/无穷范数/二值化/特征提取Key words
linear discriminant analysis/infinite norm/binarization/feature extraction分类
信息技术与安全科学引用本文复制引用
翟佳,谭龙,潘静,庞彦伟..基于无穷范数的二值线性判别分析[J].现代电子技术,2013,(22):24-27,4.基金项目
国家自然科学基金资助项目（61271412 （）
61222109） （）
