Approximate linear dependence as a design method for Kernel prototype-based classifiers

Abstract

The approximate linear dependence (ALD) method is a sparsification procedure used to build a dictionary of samples extracted from a data set. The extracted samples are approximately linearly independent in a high-dimensional kernel reproducing Hilbert space. In this paper, we argue that the ALD method itself can be used to select relevant prototypes from a training data set and use them to classify new samples using kernelized distances. The results obtained from intensive experimentation with several datasets indicate that the proposed approach is viable to be used as a standalone classifier.