A Comparison of Pruning Algorithms for Sparse Least Squares Support Vector Machines

Least Squares Support Vector Machines (LS-SVM) is a proven method for classification and function approximation. In comparison to the standard Support Vector Machines (SVM) it only requires solving a linear system, but it lacks sparseness in the number of solution terms. Pruning can therefore be applied. Standard ways of pruning the LSSVM consist of recursively solving the approximation problem and subsequently omitting data that have a small error in the previous pass and are based on support values. We suggest a slightly adapted variant that improves the performance significantly. We assess the relative regression performance of these pruning schemes in a comparison with two (for pruning adapted) subset selection schemes, -one based on the QR decomposition (supervised), one that searches the most representative feature vector span (unsupervised)-, random omission and backward selection on independent test sets in some benchmark experiments.