Least squares support vector machines and primal space estimation

In this paper a methodology for estimation in kernel-induced feature spaces is presented, making a link between the primal-dual formulation of least squares support vector machines (LS-SVM) and classical statistical inference techniques in order to perform linear regression in primal space. This is done by computing a finite dimensional approximation of the kernel-induced feature space mapping by using the Nystrom technique in primal space. Additionally, the methodology can be applied for a fixed-size formulation using active selection of the support vectors with entropy maximization in order to obtain a sparse approximation. Examples for different cases show good results.