Constructing Fast Linear Classifier with Mutual Information

Abstract

Linear classifiers are of great significance in the classification field. In this paper, with meticulous studies on the linear classifiers, we gained a general framework for constructing fast trained linear classifiers, and found the key point that dominated the performance of the classifiers. Based on that result, we brought forward a mutual information based (MI-B) linear classifier. Experiments showed that the new method achieved a striking performance improvement over all the existing fast trained linear classifiers and worked stably. In some dataset it even outperformed the linear support vector machine (LSVM)