The envisioned sixth-generation (6G) of wireless communications is expected to give rise to
the necessity of connecting very large quantities of heterogeneous wireless devices, which …

In this paper, we survey and compare different algorithms that, given an overcomplete
dictionary of elementary functions, solve the problem of simultaneous sparse signal …

Many real-world problems deal with collections of high-dimensional data, such as images,
videos, text, and web documents, DNA microarray data, and more. Often, such high …

Compressed sensing (CS) is an emerging field that has attracted considerable research
interest over the past few years. Previous review articles in CS limit their scope to standard …

A Unified Framework for High-Dimensional Analysis of M-Estimators with Decomposable
Regularizers Page 1 Statistical Science 2012, Vol. 27, No. 4, 538–557 DOI: 10.1214/12-STS400 …

We consider efficient methods for the recovery of block-sparse signals-ie, sparse signals that
have nonzero entries occurring in clusters-from an underdetermined system of linear …

Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition
of sparse or compressible signals that can be well approximated by just K¿ N elements from …

A general framework for solving image inverse problems with piecewise linear estimations is
introduced in this paper. The approach is based on Gaussian mixture models, which are …

Traditional sampling theories consider the problem of reconstructing an unknown signal x
from a series of samples. A prevalent assumption which often guarantees recovery from the …

This paper develops a theory for group Lasso using a concept called strong group sparsity.
Our result shows that group Lasso is superior to standard Lasso for strongly group-sparse …