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Iterative hard thresholding and L0 regularisation

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
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, 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

Full text not available from this repository.

More information

Published date: April 2007
Venue - Dates: IEEE International Conference on Acoustics, Speech and Signal Processing, United States, 2007-04-01
Keywords: sparse approximations, iterative thresholding, L0 regularisation
Organisations: Signal Processing & Control Grp

Identifiers

Local EPrints ID: 151925
URI: https://eprints.soton.ac.uk/id/eprint/151925
PURE UUID: 77985fb3-56a6-4fcf-9d07-134de12f9691

Catalogue record

Date deposited: 15 Jun 2010 08:27
Last modified: 17 Jul 2019 00:00

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Contributors

Author: M. Yaghoobi
Author: Michael E. Davies

University divisions

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