Linear Discriminant Regression Classification Algorithm by Fisher Criterion
ZENG Xian-hao
SHI Quan-min
Abstract：To improve the robustness of the linear regression classification (LRC) algorithm, a linear discrimi?nant regression classification algorithm based on Fisher criterion is proposed. The ratio of the between-class re?construction error over the within-class reconstruction error is maximized by Fisher criterion so as to find an opti?mal projection matrix for the LRC. Then, all testing and training images are projected to each subspace by the op?timal projection matrix and Euclidean distances between testing image and all training images are computed. Fi?nally, K-nearest neighbor classifier is used to finish face recognition . Experimental results on AR face databases show that proposed method has better recognition effects than several other regression classification approaches.
Keywords：face recognitionfisher criterionlinear discriminantlinear regression classificationK-nearest neighbor classifier
Publication Date：2015-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：3( 59-61 )