Two-parameter Fisher criterion

Abstract

This paper proposes further generalization of a multiclass Fisher's criterion. A formula describing the dependence between the generalized multiclass Fisher's criterion F(Theta) and the variance criterion F(v)Theta has been obtained. Using this formula, it has been shown that the feature extraction methods based on the Karhunen-Loeve (K-L) expansions are special cases of the discriminant method. A full evaluation of heuristic methods for feature extraction based on the K-L expansion with regard to discriminant methods has been presented. A new algorithm for sequential feature extraction has been proposed and is illustrated with an example.