Feature selection in machine learning: an exact penalty approach using a Difference of Convex function Algorithm
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| 024 | 7 | 0 | |a 10.1007/s10994-014-5455-y |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10994-014-5455-y | ||
| 245 | 0 | 0 | |a Feature selection in machine learning: an exact penalty approach using a Difference of Convex function Algorithm |h [Elektronische Daten] |c [Hoai Le Thi, Hoai Le, Tao Pham Dinh] |
| 520 | 3 | |a 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. | |
| 540 | |a The Author(s), 2014 | ||
| 690 | 7 | |a Zero-norm |2 nationallicence | |
| 690 | 7 | |a Feature selection |2 nationallicence | |
| 690 | 7 | |a Exact penalty |2 nationallicence | |
| 690 | 7 | |a DC programming |2 nationallicence | |
| 690 | 7 | |a DCA |2 nationallicence | |
| 700 | 1 | |a Le Thi |D Hoai |u Laboratory of Theoretical and Applied Computer Science (LITA EA 3097), UFR MIM, University of Lorraine, Ile du Saulcy, 57045, Metz, France |4 aut | |
| 700 | 1 | |a Le |D Hoai |u Laboratory of Theoretical and Applied Computer Science (LITA EA 3097), UFR MIM, University of Lorraine, Ile du Saulcy, 57045, Metz, France |4 aut | |
| 700 | 1 | |a Pham Dinh |D Tao |u Laboratory of Mathematics, National Institute for Applied Sciences - Rouen, University of Normandie, Avenue de l'Université, 76801, Saint-Etienne-du-Rouvray cedex, France |4 aut | |
| 773 | 0 | |t Machine Learning |d Springer US; http://www.springer-ny.com |g 101/1-3(2015-10-01), 163-186 |x 0885-6125 |q 101:1-3<163 |1 2015 |2 101 |o 10994 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10994-014-5455-y |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10994-014-5455-y |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Le Thi |D Hoai |u Laboratory of Theoretical and Applied Computer Science (LITA EA 3097), UFR MIM, University of Lorraine, Ile du Saulcy, 57045, Metz, France |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Le |D Hoai |u Laboratory of Theoretical and Applied Computer Science (LITA EA 3097), UFR MIM, University of Lorraine, Ile du Saulcy, 57045, Metz, France |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Pham Dinh |D Tao |u Laboratory of Mathematics, National Institute for Applied Sciences - Rouen, University of Normandie, Avenue de l'Université, 76801, Saint-Etienne-du-Rouvray cedex, France |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Machine Learning |d Springer US; http://www.springer-ny.com |g 101/1-3(2015-10-01), 163-186 |x 0885-6125 |q 101:1-3<163 |1 2015 |2 101 |o 10994 | ||