We provide a comprehensive review of five representative ℓ 1-minimization methods, ie,
gradient projection, homotopy, iterative shrinkage-thresholding, proximal gradient, and …

Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …

As a recently proposed technique, sparse representation based classification (SRC) has
been widely used for face recognition (FR). SRC first codes a testing sample as a sparse …

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 …

This paper demonstrates theoretically and empirically that a greedy algorithm called
Orthogonal Matching Pursuit (OMP) can reliably recover a signal with m nonzero entries in …

Many problems in signal processing and statistical inference involve finding sparse
solutions to under-determined, or ill-conditioned, linear systems of equations. A standard …

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 …

We present a framework for solving the large-scale \ell_1-regularized convex minimization
problem: \min‖x‖_1+μf(x). Our approach is based on two powerful algorithmic ideas …

The minimum lscr 1-norm solution to an underdetermined system of linear equations y= Ax
is often, remarkably, also the sparsest solution to that system. This sparsity-seeking property …