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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F19%3A00006125" target="_blank" >RIV/46747885:24220/19:00006125 - isvavai.cz</a>

Alternative codes found

RIV/67985556:_____/19:00500102

Result on the web

<a href="https://asap.ite.tul.cz/wp-content/uploads/sites/3/2018/12/ICEvXII.pdf" target="_blank" >https://asap.ite.tul.cz/wp-content/uploads/sites/3/2018/12/ICEvXII.pdf</a>

DOI - Digital Object Identifier

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

Result language

angličtina

Original language name

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

Original language description

We revise the problem of extracting one inde-pendent 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 de-mixing matrix. The separation is based on the non-Gaussianity of the source of interest, while the remaining background signalsare assumed to be Gaussian. Three gradient-based estimation algorithms are derived using the maximum likelihood principleand 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 onthe 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 interestis dominant or weak. Here, our proposed modifications of the gradient methods, taking into account the dominance/weakness of the source, show improved global convergence.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10102 - Applied mathematics

Project

<a href="/en/project/GA17-00902S" target="_blank" >GA17-00902S: Advanded Joint Blind Source Separation Methods</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

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

Name of the periodical

IEEE Transactions on Signal Processing

ISSN

1053-587X

e-ISSN

—

Volume of the periodical

67

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

1050-1064

UT code for WoS article

000455720600015

EID of the result in the Scopus database

2-s2.0-85058892691