Classification by Linearity Assumption

Abstract

Recently a classifier was proposed that was based on the assumption: the training samples for a particular class form a linear basis for any new test sample. This assumption is a generalization of the Nearest Neighbour classifier. In the previous work, the classifier was built upon this assumption required solving a complex optimisation problem. The optimisation method was time consuming and restrictive in application. In this work our proposed algorithm takes care of the previous problems keeping the basic assumption intact. We also offer generalisations of the basic assumption. Comparative experimental results on some UCI machine learning databases show that our proposed generalised classifier is performs as good as other well known techniques like Nearest Neighbour and Support Vector Machine.