Gradient Algorithms for Complex Non-Gaussian Independent Component/Vector Extraction, Question of Convergence

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F19%3A00500102" target="_blank" >RIV/67985556:_____/19:00500102 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/46747885:24220/19:00006125

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8579170" target="_blank" >https://ieeexplore.ieee.org/document/8579170</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2018.2887185" target="_blank" >10.1109/TSP.2018.2887185</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Gradient Algorithms for Complex Non-Gaussian Independent Component/Vector Extraction, Question of Convergence

Popis výsledku v původním jazyce

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.n

Název v anglickém jazyce

Gradient Algorithms for Complex Non-Gaussian Independent Component/Vector Extraction, Question of Convergence

Popis výsledku anglicky

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.n

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA17-00902S" target="_blank" >GA17-00902S: Pokročilé metody slepé separace podprostorů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Signal Processing

ISSN

1053-587X

e-ISSN

—

Svazek periodika

67

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

1050-1064

Kód UT WoS článku

000455720600015

EID výsledku v databázi Scopus

2-s2.0-85058892691