Computational methods for sparse solution of linear inverse problems
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 …
linear combination of a few elementary signals drawn from a fixed collection. This paper …
Signal recovery from random measurements via orthogonal matching pursuit
JA Tropp, AC Gilbert - IEEE Transactions on information theory, 2007 - ieeexplore.ieee.org
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 …
Orthogonal Matching Pursuit (OMP) can reliably recover a signal with m nonzero entries in …
CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
Compressive sampling offers a new paradigm for acquiring signals that are compressible
with respect to an orthonormal basis. The major algorithmic challenge in compressive …
with respect to an orthonormal basis. The major algorithmic challenge in compressive …
CoSaMP: iterative signal recovery from incomplete and inaccurate samples
Compressive sampling (CoSa) is a new paradigm for developing data sampling
technologies. It is based on the principle that many types of vector-space data are …
technologies. It is based on the principle that many types of vector-space data are …
Precise undersampling theorems
Undersampling theorems state that we may gather far fewer samples than the usual
sampling theorem while exactly reconstructing the object of interest-provided the object in …
sampling theorem while exactly reconstructing the object of interest-provided the object in …
A stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework
J Jin, Y Gu, S Mei - IEEE Journal of Selected Topics in Signal …, 2010 - ieeexplore.ieee.org
Based on the methodological similarity between sparse signal reconstruction and system
identification, a new approach for sparse signal reconstruction in compressive sensing (CS) …
identification, a new approach for sparse signal reconstruction in compressive sensing (CS) …
Tree-structured compressive sensing with variational Bayesian analysis
L He, H Chen, L Carin - IEEE Signal Processing Letters, 2009 - ieeexplore.ieee.org
In compressive sensing (CS) the known structure in the transform coefficients may be
leveraged to improve reconstruction accuracy. We here develop a hierarchical statistical …
leveraged to improve reconstruction accuracy. We here develop a hierarchical statistical …
The group lasso for stable recovery of block-sparse signal representations
Group Lasso is a mixed l 1/l 2-regularization method for a block-wise sparse model that has
attracted a lot of interests in statistics, machine learning, and data mining. This paper …
attracted a lot of interests in statistics, machine learning, and data mining. This paper …
A* orthogonal matching pursuit: Best-first search for compressed sensing signal recovery
NB Karahanoglu, H Erdogan - Digital Signal Processing, 2012 - Elsevier
Compressed sensing is a developing field aiming at the reconstruction of sparse signals
acquired in reduced dimensions, which make the recovery process under-determined. The …
acquired in reduced dimensions, which make the recovery process under-determined. The …