关键词: 线性判别分析, 特征提取, Fisher准则函数, 高斯混合模型

Abstract: Fisher criterion assumes that each sample has a unimodal,symmetric distribution for each class.However,when the training sample size is large and their distributions are multimodal or skewed,the definition of classical Fisher criterion can not correctly reflect the distribution of the data in the sample space,in that case,the sets of discriminant vectors are not optimal virtually.Aiming at this case,this paper generalizes the Fisher criterion by introducing the idea of Gaussian mixture model and proposes a new linear discriminant analysis,at same time,a direct algorithm is proposed.The method is applied to letter image recognition,and the experimental result shows that the present method is superior to the existing methods in terms of correct classification rate.

Key words: linear discriminant analysis, feature extraction, Fisher criterion, Gaussian mixture model