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» One sketch for all: fast algorithms for compressed sensing
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STOC
2007
ACM
106views Algorithms» more  STOC 2007»
14 years 11 months ago
One sketch for all: fast algorithms for compressed sensing
Compressed Sensing is a new paradigm for acquiring the compressible signals that arise in many applications. These signals can be approximated using an amount of information much ...
Anna C. Gilbert, Martin J. Strauss, Joel A. Tropp,...
TIP
2010
127views more  TIP 2010»
13 years 9 months ago
Bayesian Compressive Sensing Using Laplace Priors
In this paper we model the components of the compressive sensing (CS) problem, i.e., the signal acquisition process, the unknown signal coefficients and the model parameters for ...
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag...
COLT
2010
Springer
13 years 9 months ago
Deterministic Sparse Fourier Approximation via Fooling Arithmetic Progressions
A significant Fourier transform (SFT) algorithm, given a threshold and oracle access to a function f, outputs (the frequencies and approximate values of) all the -significant Fou...
Adi Akavia
JC
2007
119views more  JC 2007»
13 years 11 months ago
Deterministic constructions of compressed sensing matrices
Compressed sensing is a new area of signal processing. Its goal is to minimize the number of samples that need to be taken from a signal for faithful reconstruction. The performan...
Ronald A. DeVore
SIAMIS
2011
13 years 6 months ago
NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
Abstract. Accurate signal recovery or image reconstruction from indirect and possibly undersampled data is a topic of considerable interest; for example, the literature in the rece...
Stephen Becker, Jérôme Bobin, Emmanue...