Feature selection in SVM via polyhedral k-norm

We treat the feature selection problem in the support vector machine (SVM) framework by adopting an optimization model based on use of the 0 pseudo-norm. The objective is to control the number of non-zero components of the normal vector to the separating hyperplane, while maintaining satisfactory classification accuracy. In our model the polyhedral norm parallel to.parallel to [k], intermediate between parallel to.parallel to 1 and parallel to.parallel to 8, plays a significant role, allowing us to come out with a DC (difference of convex) optimization problem that is tackled by means of DCA algorithm. The results of several numerical experiments on benchmark classification datasets are reported.