bibtype I - Internal Report
ARLID 0411389
utime 20240103182323.9
mtime 20060210235959.9
title (primary) (eng) Efficient Variant of Algorithm FastICA for Independent Component Analysis Attaining the Cramér-Rao Lower Bound
publisher
place Praha
name ÚTIA AV ČR
pub_time 2005
specification
page_count 6 s.
edition
part_name Interní publikace DAR - ÚTIA.
volume_id 2005/9
keyword blind signal separation
keyword linear mixture
author (primary)
ARLID cav_un_auth*0108100
name1 Koldovský
name2 Zbyněk
institution UTIA-B
full_dept Department of Stochastic Informatics
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0101212
name1 Tichavský
name2 Petr
institution UTIA-B
full_dept Department of Stochastic Informatics
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
COSATI 12B
cas_special
project
project_id 2005
agency GA MŠk
ARLID cav_un_auth*0213225
research CEZ:AV0Z10750506
abstract (eng) We propose an improved version of algorithm FastICA which is asymptotically efficient, i.e., its accuracy attains the Cramér-Rao lower bound provided that the probability distribution of the signal components belongs to the class of generalized Gaussian distribution. Its computational complexity is only slightly (about three times) higher than that of ordinary symmetric FastICA. Simulation section shows superior performance of the algorithm compared with JADE, and of non-parametric ICA.
abstract (cze) V práci je navržena nová varianta algoritmu FastICA, která je asymptoticky eficientní, tj. její přesnost se blíží Rao-Cramerově hranici, za předpokladu že pravděpodobnostní rozložení separovaných signálů je z třídy zobecněných Gaussovských distribucí. V simulacích je navržená metoda porovnávána se známým algoritmem JADE a s neparametrickou ICA.
RIV BB
reportyear 2006
department SI
permalink http://hdl.handle.net/11104/0131471
ID_orig UTIA-B 20050119
arlyear 2005
mrcbU10 2005
mrcbU10 Praha ÚTIA AV ČR