Regularized dictionary learning for sparse approximation


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.

Download

Full text not available from this repository.

Description/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.

Item Type: Conference or Workshop Item (Paper)
Venue - Dates: 16th Annual European Signal Processing Conference (EUSIPCO), Switzerland, 2008-08-25 - 2008-08-29
Related URLs:
Subjects:
Organisations: Signal Processing & Control Grp
ePrint ID: 151913
Date :
Date Event
25 August 2008Published
Date Deposited: 13 May 2010 09:00
Last Modified: 18 Apr 2017 04:20
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/151913

Actions (login required)

View Item View Item