Sparse semi-supervised support vector machines by DC programming and DCA
Two DC (Difference of Convex) Algorithms based approaches are investigated for feature selection in S3VM using the zero norm. The first is DC approximation approach that approximates the zero norm by a DC function. Five approximations are considered. The second is an exact reformulation approach based on exact penalty techniques in DC programming. All the resulting problems are DC programs for which six versions of DCA are developed. A careful empirical experiment on several benchmark datasets are performed.
dc programming and dca feature selection dc approximation exact penalty semi-supervised svm non-convex optimization