An improved kernel Fisher discriminant classifier and its applications

In order to use kernel Fisher discriminant (KFD) classifiers to solve large-scale learning problems, this paper decomposes an n-class dataset into n two-class subsets, and use a subset only composed of a small part of the original dataset in determining the structure of a single KFD classifier. The large number of samples in a class can be further represented by only a small number of prototypes with changeable widths, which are on behalf of kernels. Training samples are not certainly linearly separable in the kernel space, so additional expansive and contractive transformation is needed. Sigmoid functions can be use to implement such tasks. The results of two-spirals and letter recognition show that the proposed method is quite effective.