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Regularized dictionary learning for sparse approximation

Regularized dictionary learning for sparse approximation
Regularized dictionary learning for sparse approximation
Sparse signal models approximate signals using a small number of
elements from a large set of vectors, called a dictionary. The success
of such methods relies on the dictionary fitting the signal structure.
Therefore, the dictionary has to be designed to fit the signal
class of interest. This paper uses a general formulation that allows
the dictionary to be learned form the data with some a priori information
about the dictionary. In this formulation a universal cost
function is proposed and practical algorithms are presented to minimize
this cost under different constraints on the dictionary. The
proposed methods are compared with previous approaches using
synthetic and real data. Simulations highlight the advantages of the
proposed methods over other currently available dictionary learning
strategies.
Yaghoobi, M.
093ccfdd-9ba5-4d02-abb1-7f341eed070d
Blumensath, T.
470d9055-0373-457e-bf80-4389f8ec4ead
Davies, M.
ad39b2b8-121a-49ee-8e4a-daf601ba7fe6
Yaghoobi, M.
093ccfdd-9ba5-4d02-abb1-7f341eed070d
Blumensath, T.
470d9055-0373-457e-bf80-4389f8ec4ead
Davies, M.
ad39b2b8-121a-49ee-8e4a-daf601ba7fe6

Yaghoobi, M., Blumensath, T. and Davies, M. (2008) Regularized dictionary learning for sparse approximation. 16th Annual European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland. 24 - 28 Aug 2008.

Record type: Conference or Workshop Item (Paper)

Abstract

Sparse signal models approximate signals using a small number of
elements from a large set of vectors, called a dictionary. The success
of such methods relies on the dictionary fitting the signal structure.
Therefore, the dictionary has to be designed to fit the signal
class of interest. This paper uses a general formulation that allows
the dictionary to be learned form the data with some a priori information
about the dictionary. In this formulation a universal cost
function is proposed and practical algorithms are presented to minimize
this cost under different constraints on the dictionary. The
proposed methods are compared with previous approaches using
synthetic and real data. Simulations highlight the advantages of the
proposed methods over other currently available dictionary learning
strategies.

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More information

Published date: 25 August 2008
Venue - Dates: 16th Annual European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland, 2008-08-24 - 2008-08-28
Organisations: Signal Processing & Control Grp

Identifiers

Local EPrints ID: 151913
URI: http://eprints.soton.ac.uk/id/eprint/151913
PURE UUID: e0c1bef6-635b-465f-b262-39daac9fe0a1
ORCID for T. Blumensath: ORCID iD orcid.org/0000-0002-7489-265X

Catalogue record

Date deposited: 13 May 2010 09:00
Last modified: 24 Mar 2022 02:39

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Contributors

Author: M. Yaghoobi
Author: T. Blumensath ORCID iD
Author: M. Davies

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