- Feature selection in SVM via polyhedral k-norm Manlio Gaudioso (manlio.gaudiosounical.it) Enrico Gorgone (egorgoneunica.it) Jean-Baptiste Hiriart-Urruty (jbhumath.univ-toulouse.fr) Abstract: We treat the Feature Selection problem in the Support Vector Machine (SVM) framework by adopting an optimization model based on use of the $\ell_0$ pseudo--norm. The objective is to control the number of non-zero components of normal vector to the separating hyperplane, while maintaining satisfactory classification accuracy. In our model the polyhedral norm $\|.\|_{[k]}$, intermediate between $\|.\|_1$ and $\|.\|_{\infty}$, 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. Keywords: Sparse optimization, Cardinality constraint, k-norm, Support Vector Machine, DC optimization Category 1: Global Optimization Category 2: Applications -- Science and Engineering (Data-Mining ) Citation: Optimization Letters, to appear Download: Entry Submitted: 11/11/2018Entry Accepted: 11/12/2018Entry Last Modified: 09/10/2019Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.