4,939 research outputs found
Conjugate Gradient Iterative Hard Thresholding:\ud Observed Noise Stability for Compressed Sensing
Conjugate Gradient Iterative Hard Thresholding (CGIHT) for compressed sensing combines the low per iteration complexity of fast greedy sparse approximation algorithms with the improved convergence rates of more complicated, projection based algorithms. This article shows that CGIHT is robust to\ud
additive noise and is typically the fastest greedy algorithm in the presence of noise
A robust parallel algorithm for combinatorial compressed sensing
In previous work two of the authors have shown that a vector with at most nonzeros can be recovered from an expander
sketch in operations via the
Parallel- decoding algorithm, where denotes the
number of nonzero entries in . In this paper we
present the Robust- decoding algorithm, which robustifies
Parallel- when the sketch is corrupted by additive noise. This
robustness is achieved by approximating the asymptotic posterior distribution
of values in the sketch given its corrupted measurements. We provide analytic
expressions that approximate these posteriors under the assumptions that the
nonzero entries in the signal and the noise are drawn from continuous
distributions. Numerical experiments presented show that Robust- is
superior to existing greedy and combinatorial compressed sensing algorithms in
the presence of small to moderate signal-to-noise ratios in the setting of
Gaussian signals and Gaussian additive noise
One-Bit Compressed Sensing by Greedy Algorithms
Sign truncated matching pursuit (STrMP) algorithm is presented in this paper.
STrMP is a new greedy algorithm for the recovery of sparse signals from the
sign measurement, which combines the principle of consistent reconstruction
with orthogonal matching pursuit (OMP). The main part of STrMP is as concise as
OMP and hence STrMP is simple to implement. In contrast to previous greedy
algorithms for one-bit compressed sensing, STrMP only need to solve a convex
and unconstraint subproblem at each iteration. Numerical experiments show that
STrMP is fast and accurate for one-bit compressed sensing compared with other
algorithms.Comment: 16 pages, 7 figure
- β¦