Abstract
This paper addresses the problem of feature selection for Multi-class Support Vector Machines (MSVM). Basing on the l 0 and the l 2-l 0 regularization we consider two models for this problem. The l 0-norm is approximated by a suitable way such that the resulting optimization problems can be expressed as DC (Difference of Convex functions) programs for which DC programming and DC Algorithms (DCA) are investigated. The preliminary numerical experiments on real-world datasets show the efficiency and the superiority of our methods versus one of the best standard algorithms on booth feature selection and classification.
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