Large Margin Classifier Based on Hyperdisks

This paper introduces a binary large margin classifier that approximates each class with an hyper disk constructed from its training samples. For any pair of classes approximated with hyper disks, there is a corresponding linear separating hyper plane that maximizes the margin between them, and this can be found by solving a convex program that finds the closest pair of points on the hyper disks. More precisely, the best separating hyper plane is chosen to be the one that is orthogonal to the line segment connecting the closest points on the hyper disks and at the same time bisects the line. The method is extended to the nonlinear case by using the kernel trick, and the multi-class classification problems are dealt with constructing and combining several binary classifiers as in Support Vector Machine (SVM) classifier. The experiments on several databases show that the proposed method compares favorably to other popular large margin classifiers.