Iterative hard thresholding and L0 regularisation
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 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
sparse approximations, iterative thresholding, L0 regularisation
Blumensath, Thomas
470d9055-0373-457e-bf80-4389f8ec4ead
Yaghoobi, M.
093ccfdd-9ba5-4d02-abb1-7f341eed070d
Davies, Michael E.
bbe1dd72-273c-445f-9540-507809816198
April 2007
Blumensath, Thomas
470d9055-0373-457e-bf80-4389f8ec4ead
Yaghoobi, M.
093ccfdd-9ba5-4d02-abb1-7f341eed070d
Davies, Michael E.
bbe1dd72-273c-445f-9540-507809816198
Blumensath, Thomas, Yaghoobi, M. and Davies, Michael E.
(2007)
Iterative hard thresholding and L0 regularisation.
IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu, United States.
Record type:
Conference or Workshop Item
(Paper)
Abstract
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
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More information
Published date: April 2007
Venue - Dates:
IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu, United States, 2007-03-31
Keywords:
sparse approximations, iterative thresholding, L0 regularisation
Organisations:
Signal Processing & Control Grp
Identifiers
Local EPrints ID: 151925
URI: http://eprints.soton.ac.uk/id/eprint/151925
PURE UUID: 77985fb3-56a6-4fcf-9d07-134de12f9691
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Date deposited: 15 Jun 2010 08:27
Last modified: 24 Mar 2022 02:39
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Contributors
Author:
M. Yaghoobi
Author:
Michael E. Davies
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