Robust Polyhedral Regularization

Abstract

In this paper, we establish robustness to noise perturbations of polyhedral regularization of linear inverse problems. We provide a sufficient condition that ensures that the polyhedral face associated to the true vector is equal to that of the recovered one. This criterion also implies that the ℓ2 recovery error is proportional to the noise level for a range of parameter. Our criterion is expressed in terms of the hyperplanes supporting the faces of the unit polyhedral ball of the regularization. This generalizes to an arbitrary polyhedral regularization results that are known to hold for sparse synthesis and analysis ℓ1 regularization which are encompassed in this framework. As a byproduct, we obtain recovery guarantees for ℓ∞ and ℓ1−ℓ∞ regularization.

Publication
10th international conference on Sampling Theory and Applications (SampTA 2013)