Multiclass Classifiers Based on Dimension Reduction with Generalized LDA
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Abstract
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 which 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 algorithms, taking advantage of the dimension reducing transformation
matrix without requiring additional training or any parameter optimization. A
marginal linear discriminant classifier, a Bayesian linear discriminant classifier, and a one-dimensional
Bayesian linear discriminant classifier are introduced for multiclass classification.
Our experimental results illustrate that these classifiers produce higher ten-fold cross
validation accuracy than kNN and centroid based classification in the reduced dimensional
space providing efficient general multiclass classifiers.
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2006-01-27
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Technical Report