eprintid: 1528
rev_number: 7
eprint_status: archive
userid: 45
dir: disk0/00/00/15/28
datestamp: 2013-03-07 13:30:21
lastmod: 2013-03-12 14:58:11
status_changed: 2013-03-07 13:30:21
type: book_section
metadata_visibility: show
creators_name: Rusu, Cristian
creators_id: cristian.rusu@imtlucca.it
title: Fast design of efficient dictionaries for sparse representations
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
keywords: K-SVD algorithm, T-mindot, clustering, large datasets training, representation errors, signal processing, sparse representations 
abstract: One of the central issues in the field of sparse representations is the design of overcomplete dictionaries with a fixed sparsity level from a given dataset. This article describes a fast and efficient procedure for the design of such dictionaries. The method implements the following ideas: a reduction technique is applied to the initial dataset to speed up the upcoming procedure; the actual training procedure runs a more sophisticated iterative expanding procedure based on K-SVD steps. Numerical experiments on image data show the effectiveness of the proposed design strategy.
date: 2012
date_type: published
publisher: IEEE
pagerange: 1-5
event_title: Machine Learning for Signal Processing (MLSP), 2012 IEEE International Workshop on
id_number: 10.1109/MLSP.2012.6349795
refereed: TRUE
isbn: 978-1-4673-1025-3
book_title: IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2012
official_url: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6349795&isnumber=6349703
citation:   Rusu, Cristian  Fast design of efficient dictionaries for sparse representations.   In:  IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2012.   IEEE, pp. 1-5.  ISBN 978-1-4673-1025-3      (2012)