부산대학교 도서관

We tackle the sparsity constrained optimization problem by resorting to polyhedral k-norm as a valid tool to emulate the ℓ 0 -pseudo-norm. The main novelty of the approach is the use of the dual of the k-norm, which allows to obtain a formulation amenable for a relaxation that can be efficiently handled by block coordinate methods. The advantage of the approach is that it does not require the solution of difference-of-convex programmes, unlike other k-norm based methods available in the literature. In fact, our block coordinate approach requires, at each iteration, the solution of two convex programmes, one of which can be solved in O ( n log ⁡ n ) time. We apply the method to feature selection within the framework of Support Vector Machine classification, and we report the results obtained on some benchmark test problems. [ABSTRACT FROM AUTHOR]