0492879 20240111141005.520180905235959.9 85048543885 10.1007/978-3-319-93764-9_16 10 s. E cav_un_epca*0492878978-3-319-93763-20302-9743161-170ChamSpringer2018DevilleYannickGannotSharonMasonRussellPlumbleyMark D.WardDominic Independent Component Analysis Blind source separation blind source extraction cav_un_auth*0230113 Koldovský Z. CZ cav_un_auth*0101212 Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics Tichavský Petr UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0363725 Ono N. JP http://library.utia.cas.cz/separaty/2018/SI/tichavsky-0492879.pdf 282 kB GA17-00902S GA ČR cav_un_auth*0345929 We propose a new algorithm for Independent Component Extraction that extracts one non-Gaussian component and is capable to exploit the non-Gaussianity of background signals without decomposing them into independent components. The algorithm is suitable for situations when the signal to be extracted is determined through initialization, it shows an extra stable convergence when the target component is dominant. In simulations, the proposed method is compared with Natural Gradient and One-unit FastICA, and it yields improved results in terms of the Signal-to-Interference ratio and the number of successful extractions. cav_un_auth*0363587 Latent Variable Analysis and Signal Separation 20180702 Guilford GB 20180705 BB 10000 10100 10103 2019 3 PR RVO:67985556 http://hdl.handle.net/11104/0286552 S 0.339 Q2 Q2 2018 85048543885 SCOPUS PUBMED WOS 282 kB cav_un_epca*0492878 Latent Variable Analysis and Signal Separation 978-3-319-93763-2 0302-9743 1611-3349 161 170 Cham Springer 2018 Lecture Notes in Computer Science 10891 340 Deville Yannick 340 Gannot Sharon 340 Mason Russell 340 Plumbley Mark D. 340 Ward Dominic