Sparse Solution Of Underdetermined Linear Equations By Stagewise Orthogonal Matching Pursuit

By David L. Donoho. 2006

899 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b6a1617f0e341205b684a84002a720495c017c88

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Citations in the corpus

Compressed Sensing MRI (2008, background): “After subtracting the calculated interference, smaller components, previously submerged in interference, rise above it and can be recovered [25].”
Inpainting and Zooming Using Sparse Representations (2009, methodology): “This procedure is also closely related to homotopy continuation and path following methods [66, 67 ].”
From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images (2009, background): “Handling of the general case is somewhat more involved, as can be seen in [76, 75, 32, 62, 5, 67, 59].”
Bayesian Compressive Sensing (2008, methodology): “This motivates development of a fast RVM algorithm with the objective of achieving highly efﬁcient computations that are comparable to existing CS algorithms (e.g., OMP [9] and StOMP [10]).”
Fast Solution of $\ell _{1}$ -Norm Minimization Problems When the Solution May Be Sparse (2008, background): “ideas; examples include [23], [11], [47], [48], [27]; related ideas”
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit (2007, methodology): “In this setting, other types of greedy algorithms, such as StOMP [ 34 ] or ROMP [33], can alleviate the computational burden by reducing the number of least squares problems that need to be solved.”
Bayesian Compressive Sensing Via Belief Propagation (2008, background): “Large and small magnitudes correspond to zero mean Gaussian distributions with high and low variances, which are implied by and , respectively, with .”
Simply denoise: Wavefield reconstruction via jittered undersampling (2008, methodology): “…`1 solvers such as iterated re-weighted least-squares (IRLS - Gersztenkorn et al., 1986), stage-wise orthogonal matching pursuit (StOMP - Donoho et al., 2006), and iterative soft-thresholding with cooling (Hennenfent and Herrmann, 2005; Herrmann and Hennenfent, 2007) derived from…”
Fixed-Point Continuation for l1-Minimization: Methodology and Convergence (2008, methodology): “We have conducted a comprehensive computational study to compare our fixedpoint continuation (FPC) algorithm with three recent state-of-the-art compressed sensing recovery algorithms [17, 28, 35].”
Approximation accuracy, gradient methods, and error bound for structured convex optimization (2010, background): “, Gaussian), this condition holds with high probability (for N0 in the order of n to within log factors); see [26, 28, 39, 45, 111, 135] and references therein for the noiseless case and [27, 38] for the noisy case.”
Precise Undersampling Theorems (2010, background): “922 Proceedings of the IEEE |V ol. 98, No. 6, June 2010 discussed here. (The theory in [ 33 ] is semi-empirical.”
Model-Based Compressive Sensing (2008, methodology): “Our goal is to push M as close as possible to K in order to perform as much signal “compression” during acquisition as possible.”
Sparse Coding via Thresholding and Local Competition in Neural Circuits (2008, methodology): “…Rao, 2004; Tipping, 2001), FOCUSS (Rao and Kreutz-Delgado, 1999), modiﬁcations of greedy algorithms that select multiple coeﬃcients on each iteration (Donoho et al., 2006; Pece and Petkov, 2000; Feichtinger et al., 1994) and MP extensions that perform an orthogonalization at each step (Davis et…”
Sparsity and persistence: mixed norms provide simple signal models with dependent coefficients (2009, unknown): “This heuristics was then generalized by the Stagewise Matching Pursuit [9].”