Multiclass linear dimension reduction by weighted pairwise Fisher criteria

We derive a class of computationally inexpensive linear dimension reduction criteria by introducing a weighted variant of the well-known It-class Fish er 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 LD A by introducing a different weighting function.