Hyperspherical Prototypes for Pattern Classification

The nearest neighbor method is one of the most widely used pattern classificationmethods. However its major drawback in practice is the curse of dimensionality. In this paper we propose a new method to alleviate this problem significantly. In this method, we attempt to cover the training patterns of each class with a number of hyperspheres. The method attempts to design hyperspheres as compact as possible, and we pose this as a quadratic optimization problem. We performed several simulation experiments, and found that the proposed approach results in considerable speed-up over the k-nearestneighbor method while maintaining the same level of accuray. It also significantly beats other prototype classification methods (Like LVQ, RCE and CCCD) in most performance aspects.