Iterative Hard Thresholding and L0 Regularisation

Sparse signal approximations are approximations that use only a small number of elementary waveforms to describe a signal. In this paper we proof the convergence of an iterative hard thresholding algorithm and show, that the fixed points of that algorithm are local minima of the sparse approximation...

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Bibliographic Details
Published in:2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07 Vol. 3; pp. III-877 - III-880
Main Authors: Blumensath, T., Yaghoobi, M., Davies, M.E.
Format: Conference Proceeding
Language:English
Published: IEEE 01.04.2007
Subjects:
Approximation algorithms
Convergence
Cost function
Equations
Iterative algorithms
Iterative Thresholding
L0 Regularisation
Matching pursuit algorithms
Noise reduction
Pursuit algorithms
Signal processing
Signal processing algorithms
Sparse Approximations
ISBN:9781424407279, 1424407273
ISSN:1520-6149
Online Access:Get full text
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Summary:Sparse signal approximations are approximations that use only a small number of elementary waveforms to describe a signal. In this paper we proof the convergence of an iterative hard thresholding algorithm and show, that the fixed points of that algorithm are local minima of the sparse approximation cost function, which measures both, the reconstruction error and the number of elements in the representation. Simulation results suggest that the algorithm is comparable in performance to a commonly used alternative method.
ISBN:9781424407279
1424407273
ISSN:1520-6149
DOI:10.1109/ICASSP.2007.366820