Gradient Pursuits

By Thomas Blumensath, M. Davies. IEEE Transactions on Signal Processing, 2008

507 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/eba2b0c5d8f7583eb0dca895f478e2acd0e98539

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In Biomedical Engineering

In Computational Mechanics

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Citations in the corpus

A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications (2018, background): “E. Davies, ‘‘Gradient pursuits,’’ IEEETrans.”
Sparse Reconstruction by Separable Approximation (2008, methodology): “Matching pursuit and orthogonal matching pursuit have also been proposed for finding sparse approximate solutions of [4], [23], [28], [72]; these methods, previously known in statistics as forward selection [76], are not based on an explicit optimization formulation.”
Resolution Enhancement for Inversed Synthetic Aperture Radar Imaging Under Low SNR via Improved Compressive Sensing (2010, methodology): “This optimization problem can be efficiently solved by both greedy algorithms such as basis pursuit [30], gradient pursuit [31],”
Compressive data gathering for large-scale wireless sensor networks (2009, background): “While there is a large body of on-going work looking for low-complexity reconstruction techniques [25][5], this topic is beyond the scope of our paper.”
Normalized Iterative Hard Thresholding: Guaranteed Stability and Performance (2010, background): “In the regime where this property fails, the algorithm is guaranteed to converge to a local minimum of the cost function (2) and empirically shows good average performance.”
Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit (2008, methodology): “A different acceleration approach recently proposed is the Gradient Pursuit algorithm [15], which employs an iterative process in order to more efficiently solve the `2 orthogonalization problem in OMP.”
False data injection attacks against state estimation in electric power grids (2011, methodology): “…algorithms have been developed to deal with this problem, for example, the Matching Pursuit algorithm [Natarajan 1995; Pati et al. 1993; Lovisolo et al. 2005]; the Basis Pursuit algorithm [Chen 1995; Georgiev and Cichoki 2004]; and the Gradient Pursuit algorithm [Blumensath and Davies 2008].”
Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation (2010, methodology): “The algorithm trains an overcomplete dictionary using blocks from the noisy signal, and then denoises the signal using this dictionary, averaging the denoised blocks when they overlap in the result.”
Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems (2007, methodology): “Finally, we mention matching pursuit (MP) and orthogonal MP (OMP) [5], [17], [20], [56], which are greedy schemes to find a sparse representation of a signal on a dictionary of functions .”