Proceedings of the 2018 2nd International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2018)

Compressive Sensing Method for Function Recovery

Authors
Aitong Huang, Renzhong Feng
Corresponding Author
Aitong Huang
Available Online May 2018.
DOI
10.2991/ammsa-18.2018.5How to use a DOI?
Keywords
function recovery; compressed sensing; l1 minimization problem; orthogonal matching pursuit algorithm
Abstract

It is well known that under certain orthogonal systems (such as Chebyshev tensor and Legendre polynomial space), the expansion coefficient of a smooth function has a sparseness that the coefficient with a finite number of coefficients after the first is gradually zero. For accurate sampling and sampling data with noise, this paper uses compressive sensing technology to recover the first limited number of function expansion coefficients, so as to achieve the purpose of function recovery. Numerical experiments show that this technique is feasible.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2018 2nd International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2018)
Series
Advances in Intelligent Systems Research
Publication Date
May 2018
ISBN
978-94-6252-529-0
ISSN
1951-6851
DOI
10.2991/ammsa-18.2018.5How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Aitong Huang
AU  - Renzhong Feng
PY  - 2018/05
DA  - 2018/05
TI  - Compressive Sensing Method for Function Recovery
BT  - Proceedings of the 2018 2nd International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2018)
PB  - Atlantis Press
SP  - 21
EP  - 26
SN  - 1951-6851
UR  - https://doi.org/10.2991/ammsa-18.2018.5
DO  - 10.2991/ammsa-18.2018.5
ID  - Huang2018/05
ER  -