Compressive Sensing Method for Function Recovery

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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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  -