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Citations per year

Number of citations per year for the bioinformatics software tool IHT
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This map represents all the scientific publications referring to IHT per scientific context
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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

 (13)
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 27F(x)=A1+ηf(∥x−x0∥2)A2, where A1∈R100×400 is a fixed Gaussian random matrix, x0∈R400 is a reference vector, f:[ […]

library_books

An Intelligent Grey Wolf Optimizer Algorithm for Distributed Compressed Sensing

2018
Comput Intell Neurosci
PMCID: 5892601
DOI: 10.1155/2018/1723191

[…] problem. 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 based […]

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 regularizations […]

library_books

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

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

[…] Spectra 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. […]

library_books

PEAR: PEriodic And fixed Rank separation for fast fMRI

2017
Med Phys
PMCID: 5836861
PMID: 28945924
DOI: 10.1002/mp.12599

[…] lve each sub‐problem Eqs., via gradient projection where the proximal gradient is used for the non‐differentiable ℓ1 function in Eq. . A solution for Eq. has been proposed by Goldfarb et al., a.k.a Iterative Hard Thresholding with Matrix Shrinkage (IHT‐MS). It consists of a gradient step for data consistency followed by a projection step onto the subspace C. The general step is:(7)An=Rr(Sμ(An−1− […]

library_books

Adaptive Integration of the Compressed Algorithm of CS and NPC for the ECG Signal Compressed Algorithm in VLSI Implementation

2017
PMCID: 5677428
PMID: 28991216
DOI: 10.3390/s17102288

[…] onstruction original vector x^ can be obtained as follows:(4)x^=Ψα^.Some algorithms, such as orthogonal matching pursuit (OMP) [], extend the orthogonal matching pursuit [], iterative hard threshold (IHT) [], and gradient pursuit (GP) [] to find an appropriate solution for α^ in (2) for CS recovery. […]


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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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