Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria

ÐWe derive a class of computationally inexpensive linear dimension reduction criteria by introducing a weighted variant of the well-known K-class Fisher criterion associated with linear discriminant analysis (LDA). It can be seen that LDA weights contributions of individual class pairs according to the Euclidian distance of the respective class means. We generalize upon LDA by introducing a different weighting function. Index TermsÐLinear dimension reduction, Fisher criterion, linear discriminant analysis, Bayes error, approximate pairwise accuracy criterion.