The theory of compressive sensing has shown that sparse signals can be reconstructed
exactly from many fewer measurements than traditionally believed necessary. In [1], it was …

Sparse Bayesian learning (SBL) and specifically relevance vector machines have received
much attention in the machine learning literature as a means of achieving parsimonious …

With solid theoretical foundations and numerous potential applications, Blind Signal
Processing (BSP) is one of the hottest emerging areas in Signal Processing. This volume …

Sparsity of representations of signals has been shown to be a key concept of fundamental
importance in fields such as blind source separation, compression, sampling and signal …

We address the problem of finding sparse solutions to an underdetermined system of
equations when there are multiple measurement vectors having the same, but unknown …

This paper considers nonconvex distributed constrained optimization over networks,
modeled as directed (possibly time-varying) graphs. We introduce the first algorithmic …

Several authors have shown recently that It is possible to reconstruct exactly a sparse signal
from fewer linear measurements than would be expected from traditional sampling theory …

This paper studies a difficult and fundamental problem that arises throughout electrical
engineering, applied mathematics, and statistics. Suppose that one forms a short linear …

Algorithms for data-driven learning of domain-specific overcomplete dictionaries are
developed to obtain maximum likelihood and maximum a posteriori dictionary estimates …

The problem of minimizing the rank of a matrix subject to affine constraints has applications
in several areas including machine learning, and is known to be NP-hard. A tractable …