摘要
线性判别分析算法是一种经典的特征提取方法,但其仅在大样本情况下适用。本文针对传统线性判别分析算法面临的小样本问题和秩限制问题,提出了一种改进的线性判别分析算法ILDA。该方法在矩阵指数的基础上,重新定义了类内离散度矩阵和类间离散度矩阵,有效地同时提取类内离散度矩阵零空间和非零空间中的信息。若干人脸数据库上的比较实验表明了ILDA在人脸识别方面的有效性。
Linear discriminant analysis（LDA） is a typical feature extraction method,but there exist at least two critical drawbacks in LDA： the small sample size problem and the rank limitation problem.In order to solve the above problems,this paper presents an improved LDA method（ILDA） which redefines the between-class scatter matrix and the within-class scatter matrix.ILDA can effectively extract the discriminative information included in the null subspace and the non-null subspace of a within-class scatter matrix.Numerical experiments on some facial databases show ILDA achieves good performance of face recognition.
出处
《计算机工程与科学》
CSCD
北大核心
2011年第7期89-93,共5页
Computer Engineering & Science
关键词
线性判别分析
类内离散度矩阵
类间离散度矩阵
人脸识别
linear discriminant analysis（LDA）
within-class scatter matrix
between-class scatter matrix
face recognition
