| Abstract: | For classifying large data sets, we propose a discriminant kernel that introduces
a nonlinear mapping from the joint space of input data and output label to a discriminant
space. Our method differs from traditional ones, which correspond to map nonlinearly from
the input space to a feature space. The induced distance of our discriminant kernel is Eu-
clidean and Fisher separable, as it is defined based on distance vectors of the feature space
to distance vectors on the discriminant space. Unlike the support vector machines or the
kernel Fisher discriminant analysis, the classifier does not need to solve a quadric program-
ming problem or eigen-decomposition problems. Therefore, it is especially appropriate to
the problems of processing large data sets. The classifier can be applied to face recognition,
shape comparison and image classification benchmark data sets. The method is significantly
faster than other methods and yet it can deliver comparable classification accuracy. |
