0500102 20240111141013.820190118235959.9 85058892691 000455720600015 10.1109/TSP.2018.2887185 15 s. P cav_un_epca*02567271053-587X674 (2019)1050-1064 Blind source separation blind source extraction independent component analysis independent vector analysis cav_un_auth*0230113 Koldovský Z. CZ 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/2019/SI/tichavsky-0500102.pdf 1727 kB https://ieeexplore.ieee.org/document/8579170 GA17-00902S GA ČR cav_un_auth*0345929 We revise the problem of extracting one independent component from an instantaneous linear mixture of signals. The mixing matrix is parameterized by two vectors: one column of the mixing matrix, and one row of the demixing matrix. The separation is based on the non-Gaussianity of the source of interest, while the remaining background signals are assumed to be Gaussian. Three gradient-based estimation algorithms are derived using the maximum likelihood principle and are compared with the Natural Gradient algorithm for Independent Component Analysis and with One-Unit FastICA based on negentropy maximization. The ideas and algorithms are also generalized to the extraction of a vector component when the extraction proceeds jointly from a set of instantaneous mixtures. Throughout this paper, we address the problem concerning the size of the region of convergence for which the algorithms guarantee the extraction of the desired source. We show that the size is influenced by the signal-to-interference ratio on the input channels. Simulations comparing several algorithms confirm this observation. They show a different size of the region of convergence under a scenario in which the source of interest is dominant or weak. Here, our proposed modificationsof the gradient methods, taking into account the dominance/weakness of the source, showimproved global convergence. WOS BB 10000 10100 10103 2020 2 4 A hod 4ah 20231122143746.0 RVO:67985556 http://hdl.handle.net/11104/0293321 1 Department of Stochastic Informatics UTIA-B 20201 ENGINEERING, ELECTRICAL & ELECTRONIC S 2 Article Multidisciplinary Sciences C ENGINEERING.ELECTRICAL&ELECTRONIC 5.217 0.871 8.7 0.05234 1.718 37840 314 2.098 43.38 6.59 9616 Q1 4.385 458 Q1 91.914 86.7 Q1* Q1* 1.73 Q1 86.654 2019 Soubory v repozitáři: tichavsky-0500102.pdf 85058892691 SCOPUS PUBMED 000455720600015 WOS 1727 kB cav_un_epca*0256727 IEEE Transactions on Signal Processing 1053-587X 1941-0476 Roč. 67 č. 4 2019 1050 1064