Local Behavior of Sparse Analysis Regularization: Applications to Risk Estimation

In this work, we study the links between the recovery proper- ties of sparse signals for Orthogonal Matching Pursuit (OMP) and the whole General MP class over nested supports.. We

It is also possible to use a dictionary D of translation invariant wavelets, so that the corresponding regularization term R A can be viewed as a multiscale (higher order)

We proposed a grounded and computationally efficient framework to unbiasedly estimate the projected risk in ℓ 1 - regularized inverse problems handling both synthesis and

Our main contribution consists in providing a geometrical interpretation of a solution with a maximal D-support, namely the fact that such a solution lives in the relative interior

Vaiter S, Deledalle C, Peyr´ e G, Fadili J, Dossal C (2012b) Local behavior of sparse analysis regularization: Applications to

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des

The set of all extreme points of C is denoted by ext(C). The unit ball of the sparse analysis regularizer. This section contains the core of our results. After giving

Section VI then presents lower and upper bounds on the source entropy using the geometric mean and the variance, thereby characteriz- ing the asymptotic rate distortion behavior of