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IHT specifications

Information


Unique identifier OMICS_25280
Name IHT
Alternative name Iterative Hard Thresholding
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Julia
License MIT License
Computer skills Advanced
Version 0.1.3
Stability Alpha
Requirements
https://github.com/klkeys/IHT.jl/archive/master.zip
Maintained Yes

Versioning


No version available

Documentation


Maintainer


  • person_outline Kevin Keys <>

Publications for Iterative Hard Thresholding

IHT citations

 (9)
library_books

Sparse signals recovered by non convex penalty in quasi linear systems

2018
PMCID: 5852208
PMID: 29576716
DOI: 10.1186/s13660-018-1652-8

[…] , we carry out a series of simulations to demonstrate the performance of ifta, and compare them with those obtained with some state-of-art methods (iterative soft thresholding algorithm (ista) [, ]), iterative hard thresholding algorithm (ihta) [, ]. in our numerical experiments, we set 27\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} […]

library_books

An Intelligent Grey Wolf Optimizer Algorithm for Distributed Compressed Sensing

2018
PMCID: 5892601
DOI: 10.1155/2018/1723191

[…] one-step greedy algorithm (osga) [] is proposed to solve the dcs problem based on jsm-1. greedy pursuit algorithms, including simultaneous orthogonal matching pursuit (somp) [], simultaneous iterative hard thresholding (siht) [], and simultaneous hard thresholding pursuit (shtp) [] are proposed to solve the dcs problem based on jsm-2. in [, ], two intelligent optimization algorithms […]

library_books

Sparse Adaptive Iteratively Weighted Thresholding Algorithm (SAITA) for Lp Regularization Using the Multiple Sub Dictionary Representation

2017
PMCID: 5751088
PMID: 29244777
DOI: 10.3390/s17122920

[…] algorithm (ita). as one of the most effective and efficient methods, the ita has been employed for many sparse recovery optimization problems due to its low computational complexities, including the iterative hard thresholding for l0 regularization [], the iterative soft thresholding for l1 regularization [] and the iterative lp thresholding for lp regularization []. l1/2 and l2/3 […]

library_books

Insight into partial agonism by observing multiple equilibria for ligand bound and Gs mimetic nanobody bound β1 adrenergic receptor

2017
PMCID: 5702606
PMID: 29176642
DOI: 10.1038/s41467-017-02008-y

[…] were recorded with 368 scans, giving an acquisition time of ~6 h. where higher sensitivity was required, multiple 6 h experiments were recorded and added. spectra were reconstructed using the iterative hard thresholding (iht) compressed sensing (cs) implementation in the cambridge cs package (m.j. bostock, unpublished). data were analysed using ccpn analysis v2., the authors declare […]

library_books

Block sparsity based joint compressed sensing recovery of multi channel ECG signals

2017
PMCID: 5437710
PMID: 28546862
DOI: 10.1049/htl.2016.0049

[…] \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$l_1$\end{document}l1 (wlm) and iterative hard thresholding (mmb-iht) algorithms were used to individually compress and reconstruct each ecg channel of mit-bih database. however, the spatially correlated information that exists […]

library_books

Accelerating functional MRI using fixed‐rank approximations and radial‐cartesian sampling

2016
PMCID: 4847647
PMID: 26777798
DOI: 10.1002/mrm.26079

[…] convention, a radial trajectory would have to use some under‐sampling to achieve an acceleration of r = 1., rank constrained optimization problems can be formulated in several different ways. the iterative hard thresholding (iht) or projected landweber algorithm solves the following constrained optimization problem using a fixed rank constraint: (1)minx‖y−φ(x)22‖ such that rank(x)=r. , […]


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IHT institution(s)
Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Division of Biostatistics, University of Southern California, Los Angeles, CA, USA; Departments of Biomathematics, Human Genetics, and Statistics, University of California, Los Angeles, CA, USA
IHT funding source(s)
Supported by National Human Genome Research Institute; Grant numbers: HG006139 and HG002536; Grant sponsor: National Institute of General Medical Sciences; Grant number: GM053275; and Grant sponsor: National Science Foundation; Grant number: DGE0707424.

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