Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit

By Joel A. Tropp, Anna C. Gilbert, Martin J. Strauss. Signal Processing, 2006

1,400 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/fb6eae80e60f190e67c4b276e98b3324cd77053c

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Citations in the corpus

Using deep stacking network to improve structured compressed sensing with Multiple Measurement Vectors (2013, background): “It is, in fact, computationally difficult because the computation ofW1 andW2 is not easy to parallelize across different machines.”
Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals (2007, methodology): “Consequently, a robust algorithm, such as M-OMP which was studied in [29], implies robustness of the entire recovery procedure.”
One-shot learning gesture recognition from RGB-D data using bag of features (2013, methodology): “This algorithm (Tropp et al., 2006; Rakotomamonjy, 2011) is shown in Algorithm 2.”
Hyperspectral Remote Sensing Data Analysis and Future Challenges (2013, background): “holds the fractional abundance vectors in its columns and x is the ith row of X [73].”
Inducing wavelets into random fields via generative boosting (2016, methodology): “The random ﬁeld model studied in this paper can be written in the form of simultaneous sparse coding [2] [19] or shared sparse coding as it was called in our previous work [24], where the training images can be encoded by a common set of wavelets selected from a given dictionary, and the…”
An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem (2007, methodology): “The simultaneous sparse approximation problem has received a lot of attention recently and several computationally feasible methods have been presented for estimating the sparse, underlying weights [5], [7], [31], [40], [50], [51].”
Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference (2010, background): “Parallel runs (p,L) (K, M) Precision matrix estimate Method K (p, p) (1, 1) Not applicable OMP [13] K (p, L) (1, 1) Not applicable Group-OMP [12] 1 (p, p) (K, 1) Identity matrix S-OMP [21] 1 (p, L) (K, M) Identity matrix MGOMP(Id) 1 (p, L) (K, M) Estimate from univariate OMP fits MGOMP(C) M (p, L) (”
Hyperspectral image classification via kernel sparse representation (2011, methodology): “In this section, we describe how the sparsity models in Section II can be extended to a feature space induced by a kernel function.”
Joint support recovery under high-dimensional scaling: Benefits and perils of ℓ 1,∞ -regularization (2008, background): “Another important case [9, 8], and the focus of this paper, is block̀1/`∞ regularization.”
Compressed Sensing for Wireless Communications: Useful Tips and Tricks (2015, methodology): “In [104], the greedy algorithm called simultaneous OMP (SOMP) is proposed.”
A Survey of Sparse Representation: Algorithms and Applications (2015, methodology): “In the literature [23, 24], from the perspective of spar e problem modeling and problem solving, sparse decompositio n algorithms are generally divided into two sections: greedy algorithms and convex relaxation algorithms.”
Bayesian sparse representation for hyperspectral image super resolution (2015, background): “The strength of the approach comes from exploiting the spatial structure in the scene, which requires processing the images in terms of spatial patches and solving a simultaneous sparse optimization problem [27].”