Approximately Normalized Iterative Hard Thresholding for Nonlinear Compressive Sensing

The nonlinear compressive sensing (NCS) is an extension of classical compressive sensing (CS) and the iterative hard thresholding (IHT) algorithm is a popular greedy-type method for solving CS. The normalized iterative hard thresholding (NIHT) is a modification of IHT and is more effective than IHT....

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Bibliographic Details
Published in:Mathematical problems in engineering Vol. 2016; no. 2016; pp. 1 - 8
Main Author: Zhu, Xunzhi
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2016
John Wiley & Sons, Inc
Subjects:
Algorithms
Approximation
Detection
Greedy algorithms
Iterative methods
Mathematical analysis
Mathematical models
Noise
Nonlinearity
Optimization
Regularization methods
Science
Signal processing
ISSN:1024-123X, 1563-5147
Online Access:Get full text
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Summary:The nonlinear compressive sensing (NCS) is an extension of classical compressive sensing (CS) and the iterative hard thresholding (IHT) algorithm is a popular greedy-type method for solving CS. The normalized iterative hard thresholding (NIHT) is a modification of IHT and is more effective than IHT. In this paper, we propose an approximately normalized iterative hard thresholding (ANIHT) algorithm for NCS by using the approximate optimal stepsize combining with Armijo stepsize rule preiteration. Under the condition similar to restricted isometry property (RIP), we analyze the condition that can identify the iterative support sets in a finite number of iterations. Numerical experiments show the good performance of the new algorithm for the NCS.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2016/2594752