eprintid: 1527
rev_number: 10
eprint_status: archive
userid: 45
dir: disk0/00/00/15/27
datestamp: 2013-03-07 13:20:08
lastmod: 2013-03-12 14:58:11
status_changed: 2013-03-07 13:20:08
type: book_section
metadata_visibility: show
creators_name: Rusu, Cristian
creators_id: cristian.rusu@imtlucca.it
title: Clustering before training large datasets - Case study: K-SVD
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: public
keywords: K-SVD algorithm, T-mindot, clustering, large datasets training, representation errors, signal processing, sparse representations 
abstract: Training and using overcomplete dictionaries has been the subject of many developments in the area of signal processing and sparse representations. The main idea is to train a dictionary that is able to achieve good sparse representations of the items contained in a given dataset. The most popular approach is the K-SVD algorithm and in this paper we study its application to large datasets. The main interest is to speedup the training procedure while keeping the representation errors close to some specific values. This goal is reached by using a clustering procedure, called here T-mindot, which reduces the size of the dataset but keeps the most representative data items and a measure of their importance. Experimental simulations compare the running times and representation errors of the training method with and without the clustering procedure and they clearly show how effective T-mindot is.
date: 2012
date_type: published
publisher: IEEE
pagerange: 2188-2192
event_title: Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European
refereed: TRUE
isbn: 978-1-4673-1068-0
book_title: Proceedings of the 20th European Signal Processing Conference (EUSIPCO), 2012
official_url: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6333907&isnumber=6333779
citation:   Rusu, Cristian  Clustering before training large datasets - Case study: K-SVD.   In:  Proceedings of the 20th European Signal Processing Conference (EUSIPCO), 2012.   IEEE, pp. 2188-2192.  ISBN 978-1-4673-1068-0      (2012)  
document_url: http://eprints.imtlucca.it/1527/1/CLUSTERING%20BEFORE%20TRAINING%20LARGE%20DATASETS%20-%20CASE%20STUDY%20K-SVD.pdf