0444036 20240103210055.020150526235959.9 10.1016/B978-0-12-802806-3.00002-6 22 s. P 296 cav_un_epca*0444035978-0-12-802806-353-74LondýnElsevier2015BinghamEllaKaskiSamuelLaaksonenJormaLampinenJouko independent component analysis blind source separation FastICA efica Cramer-Rao lower bound cav_un_auth*0108100 Koldovský Zbyněk Stochastická informatika Department of Stochastic Informatics SI SI UTIA-B Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101212 Tichavský Petr Stochastická informatika Department of Stochastic Informatics SI SI UTIA-B Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2015/SI/tichavsky-0444036.pdf GA14-13713S GA ČR CZ cav_un_auth*0303443 The article presents a survey of improved variants of the famous FastICA algorithm for Independent Component Analysis. Variants of the algorithm tailored to separate mixtures of stationary non-Gaussian signals and mixtures of nonstationary (block-wise stationary) non-Gaussian signals are described. Performance analyses of the algorithms are given and compared to the respective Cramer-Rao lower bounds. The behavior of FastICA variants when additive noise is present in the signal mixture is studied through a bias analysis. 2016 BB 2 RVO:67985556 http://hdl.handle.net/11104/0246783 S 2015 cav_un_epca*0444035 Advances in Independent Component Analysis and Learning Machines 978-0-12-802806-3 53 74 Londýn Elsevier 2015 Bingham Ella 340 Kaski Samuel 340 Laaksonen Jorma 340 Lampinen Jouko 340