the weighting between them — through the use of a mixed ℓ2ℓ1 norm as previously seen in elitist lasso. Unlike its more popular sibling ℓ1ℓ2 norm (used in group lasso), which seeks feature sparsity at the group-level, ℓ2ℓ1 norm encourages sparsity within feature groups. We demonstrate how this property can

maps and give rise to structured thresholding. These generalize Group Lasso and the previously introduced Elitist Lasso by introducing more flexibility in the coefficient domain modeling, and lead to the notion of social sparsity. The proposed operators are studied theoretically and embedded

significance maps. These generalize Group LASSO and the previously introduced Elitist LASSO by introducing more flexibility in the coefficient domain modeling. We study experimentally the performances of corresponding shrinkage operators in terms of significance map estimation in the orthogonal basis case. We

to be at least linear before termination. We emphasize the applications of our method to modeling, from a novel perspective of PLS, several statistical learning problems such as elitist Lasso, non-negative least squares and support vector machines. Numerical results on synthetic and benchmark data sets

of convergence is shown to be at least linear before termination. We emphasize the applications of our method in modeling, from a novel perspective of PLSs, some sta-tistical learning problems such as box constrained least squares, elitist Lasso (Kowalski & Torreesani, 2008) and support vector machines