caa a22 4500 605477868 CHVBK 20210128100403.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s10994-014-5455-y doi (NATIONALLICENCE)springer-10.1007/s10994-014-5455-y Feature selection in machine learning: an exact penalty approach using a Difference of Convex function Algorithm [Elektronische Daten] [Hoai Le Thi, Hoai Le, Tao Pham Dinh] We develop an exact penalty approach for feature selection in machine learning via the zero-norm $$\ell _{0}$$ ℓ 0 -regularization problem. Using a new result on exact penalty techniques we reformulate equivalently the original problem as a Difference of Convex (DC) functions program. This approach permits us to consider all the existing convex and nonconvex approximation approaches to treat the zero-norm in a unified view within DC programming and DCA framework. An efficient DCA scheme is investigated for the resulting DC program. The algorithm is implemented for feature selection in SVM, that requires solving one linear program at each iteration and enjoys interesting convergence properties. We perform an empirical comparison with some nonconvex approximation approaches, and show using several datasets from the UCI database/Challenging NIPS 2003 that the proposed algorithm is efficient in both feature selection and classification. The Author(s), 2014 Zero-norm nationallicence Feature selection nationallicence Exact penalty nationallicence DC programming nationallicence DCA nationallicence Le Thi Hoai Laboratory of Theoretical and Applied Computer Science (LITA EA 3097), UFR MIM, University of Lorraine, Ile du Saulcy, 57045, Metz, France aut Le Hoai Laboratory of Theoretical and Applied Computer Science (LITA EA 3097), UFR MIM, University of Lorraine, Ile du Saulcy, 57045, Metz, France aut Pham Dinh Tao Laboratory of Mathematics, National Institute for Applied Sciences - Rouen, University of Normandie, Avenue de l'Université, 76801, Saint-Etienne-du-Rouvray cedex, France aut Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 163-186 0885-6125 101:1-3<163 2015 101 10994 https://doi.org/10.1007/s10994-014-5455-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-014-5455-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Le Thi Hoai Laboratory of Theoretical and Applied Computer Science (LITA EA 3097), UFR MIM, University of Lorraine, Ile du Saulcy, 57045, Metz, France aut NATIONALLICENCE 700 1- Le Hoai Laboratory of Theoretical and Applied Computer Science (LITA EA 3097), UFR MIM, University of Lorraine, Ile du Saulcy, 57045, Metz, France aut NATIONALLICENCE 700 1- Pham Dinh Tao Laboratory of Mathematics, National Institute for Applied Sciences - Rouen, University of Normandie, Avenue de l'Université, 76801, Saint-Etienne-du-Rouvray cedex, France aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 163-186 0885-6125 101:1-3<163 2015 101 10994