Sparse least squares Support Vector Machine classifiers.
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TL;DR
This paper proposes a method for imposing sparseness to the LS-SVM solution by pruning the support value spectrum which is revealing the relative importance of the training data points and is immediately available as solution to the linear systems.
Abstract
In least squares support vector machine (LS-SVM) classifiers the original SVM formulation of Vapnik is modified by considering equality constraints within a form of ridge regression instead of inequality constraints. As a result the solution follows from solving a set of linear equations instead of a quadratic programming problem. However, a drawback is that sparseness is lost in the LS-SVM case due to the choice of 2-norms. In this paper we propose a method for imposing sparseness to the LS-SVM solution. This is done by pruning the support value spectrum which is revealing the relative importance of the training data points and is immediately available as solution to the linear systems.