The University of Southampton
University of Southampton Institutional Repository

Regularized dictionary learning for sparse approximation

Record type: Conference or Workshop Item (Paper)

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.

Full text not available from this repository.

Citation

Yaghoobi, M., Blumensath, T. and Davies, M. (2008) Regularized dictionary learning for sparse approximation At 16th Annual European Signal Processing Conference (EUSIPCO), Switzerland. 25 - 29 Aug 2008.

More information

Published date: 25 August 2008
Venue - Dates: 16th Annual European Signal Processing Conference (EUSIPCO), Switzerland, 2008-08-25 - 2008-08-29
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

Catalogue record

Date deposited: 13 May 2010 09:00
Last modified: 18 Jul 2017 12:55

Export record


Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×