Multiclass classifiers based on dimension reduction with generalized LDA

[摘要] Linear discriminant analysis (LDA) has been widely used for dimension reduction of data sets with multiple classes. The LDA has been recently extended to various generalized LDA methods that are applicable regardless of the relative sizes between the data dimension and the number of data items. In this paper, we propose several multiclass classifiers based on generalized LDA (GLDA) algorithms, taking advantage of the dimension reducing transformation matrix without requiring additional training or parameter optimization. A marginal linear discriminant classifier (MLDC), a Bayesian linear discriminant classifier (BLDC), and a one-dimensional BLDC are introduced for multiclass classification. Our experimental results illustrate that these classifiers produce higher ten-fold cross validation accuracy than kNN and centroid-based classifiers in the reduced dimensional space obtained from GLDA. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

[发布日期] 2007-11-01

[关键词] multiclass classifier;generalized linear discriminant analysis (LDA);generalized singular value decomposition (GSVD);dimension reduction;undersampled problems