We introduce a new method -- the group Dantzig selector -- for high dimensional sparse regression with group structure, which has a convincing theory about why utilizing the group...
The Dantzig selector is a near ideal estimator for recovery of sparse signals from linear measurements in the presence of noise. It is a convex optimization problem which can be r...
Abstract—The Dantzig selector is a recently introduced technique for near-optimal estimatation of sparse signals from a limited set of measurements. This paper offers an interpre...
—Recovery of sparse signals from noisy observations is a problem that arises in many information processing contexts. LASSO and the Dantzig selector (DS) are two well-known schem...
We introduce the Multiplicative Update Selector and Estimator (MUSE) algorithm for sparse approximation in underdetermined linear regression problems. Given f = Φα∗ + µ, the ...