Nonstationary signal analysis is one of the main topics in the field of machinery fault
diagnosis. Time–frequency analysis can identify the signal frequency components, reveals …

The goal of the sparse approximation problem is to approximate a target signal using a
linear combination of a few elementary signals drawn from a fixed collection. This paper …

In this paper we fully describe the trajectory of gradient flow over $2 $-layer diagonal linear
networks for the regression setting in the limit of vanishing initialisation. We show that the …

Edited by the people who were forerunners in creating the field, together with contributions
from 34 leading international experts, this handbook provides the definitive reference on …

In recent years there has been a growing interest in the study of sparse representation of
signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are …

A full-rank matrix \bfA∈R^n*m with n<m generates an underdetermined system of linear
equations \bfAx=\bfb having infinitely many solutions. Suppose we seek the sparsest …

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 …

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit
provides a exible and extensible interface between simulation codes and iterative analysis …

Finding sparse approximate solutions to large underdetermined linear systems of equations
is a common problem in signal/image processing and statistics. Basis pursuit, the least …

In this paper, we propose a novel sparse signal recovery algorithm called the trainable
iterative soft thresholding algorithm (TISTA). The proposed algorithm consists of two …