Solving non-convex lasso type problems with DC programming

The paper proposes a novel algorithm for addressing variable selection (or sparsity recovering) problem using non-convex penalties. A generic framework based on a DC programming is presented and yields to an iterative weighted lasso-type problem. We have then showed that many existing approaches for solving such a non-convex problem are particular cases of our algorithm. We also provide some empirical evidence that our algorithm outperforms existing ones.