Sparse non-negative signal reconstruction using fraction function penalty

Many practical problems in the real world can be formulated as the non-negative $\ell _0$ℓ0-minimisation problems, which seek the sparsest non-negative signals to underdetermined linear equations. They have been widely applied in signal and image processing, machine learning, pattern recognition and...

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Bibliographic Details
Published in:	IET signal processing Vol. 13; no. 2; pp. 125 - 132
Main Authors:	Cui, Angang, Peng, Jigen, Li, Haiyang, Wen, Meng
Format:	Journal Article
Language:	English
Published:	The Institution of Engineering and Technology 01.04.2019
Subjects:	computational complexity computer vision fraction function penalty image processing iterative methods learning (artificial intelligence) machine learning minimisation nonconvex fraction function nonnegative fraction function minimisation problem nonnegative iterative thresholding nonnegative signal recovery problems NP‐hard pattern recognition Research Article signal processing signal reconstruction sparse nonnegative signal reconstruction underdetermined linear equations image processing iterative methods NP-hard nonnegative iterative thresholding signal processing nonnegative signal recovery problems machine learning fraction function penalty nonnegative fraction function minimisation problem computer vision underdetermined linear equations signal reconstruction sparse nonnegative signal reconstruction minimisation learning (artificial intelligence) nonconvex fraction function computational complexity pattern recognition
ISSN:	1751-9675, 1751-9683, 1751-9683
Online Access:	Get full text
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