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ICA based on Split Generalized Gaussian

Publication date: 2019

Schedae Informaticae, 2019, Volume 28, pp. 25 - 47

https://doi.org/10.4467/20838476SI.19.002.14379

Authors

,
Przemysław Spurek
Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland
https://orcid.org/https://orcid.org/0000-0003-0097-5521 Orcid
All publications →
,
Przemysław Rola
Department of Mathematics of the Cracow University of Economics, Cracow, Poland
https://orcid.org/0000-0003-2886-7151 Orcid
All publications →
,
Jacek Tabor
Faculty of Mathematics and Computer Science, Jagiellonian University ul. Łojasiewicza 6, 30-348 Kraków, Poland
https://orcid.org/0000-0001-6652-7727 Orcid
All publications →
,
Aleksander Czechowski
Delft University of Technology, Delft, The Netherlands
https://orcid.org/0000-0002-6054-9842 Orcid
All publications →
Andrzej Bedychaj
Faculty of Mathematics and Computer Science, Jagiellonian University ul. Łojasiewicza 6, 30-348 Kraków, Poland
https://orcid.org/0000-0001-9416-3991 Orcid
All publications →

Titles

ICA based on Split Generalized Gaussian

Abstract

Independent Component Analysis (ICA) is a method for searching the linear transformation that minimizes the statistical dependence between its components. Most popular ICA methods use kurtosis as a metric of independence (non-Gaussianity) to maximize, such as FastICA and JADE. However, their assumption of fourth-order moment (kurtosis) may not always be satisfied in practice. One of the possible solution is to use third-order moment (skewness)  instead of kurtosis, which was applied in ICA_SG and EcoICA. In this paper we present a competitive approach to ICA based on the Split Generalized Gaussian distribution (SGGD), which is well adapted to heavy-tailed as well as asymmetric data. Consequently, we obtain a method which works better than the classical approaches, in both cases: heavy tails and non-symmetric data.

References


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Information

Information: Schedae Informaticae, 2019, Volume 28, pp. 25 - 47

Article type: Original article

Titles:

Polish:

ICA based on Split Generalized Gaussian

English:

ICA based on Split Generalized Gaussian

Authors

https://orcid.org/https://orcid.org/0000-0003-0097-5521

Przemysław Spurek
Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland
https://orcid.org/https://orcid.org/0000-0003-0097-5521 Orcid
All publications →

Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland

https://orcid.org/0000-0003-2886-7151

Przemysław Rola
Department of Mathematics of the Cracow University of Economics, Cracow, Poland
https://orcid.org/0000-0003-2886-7151 Orcid
All publications →

Department of Mathematics of the Cracow University of Economics, Cracow, Poland

https://orcid.org/0000-0001-6652-7727

Jacek Tabor
Faculty of Mathematics and Computer Science, Jagiellonian University ul. Łojasiewicza 6, 30-348 Kraków, Poland
https://orcid.org/0000-0001-6652-7727 Orcid
All publications →

Faculty of Mathematics and Computer Science, Jagiellonian University ul. Łojasiewicza 6, 30-348 Kraków, Poland

https://orcid.org/0000-0002-6054-9842

Aleksander Czechowski
Delft University of Technology, Delft, The Netherlands
https://orcid.org/0000-0002-6054-9842 Orcid
All publications →

Delft University of Technology, Delft, The Netherlands

https://orcid.org/0000-0001-9416-3991

Andrzej Bedychaj
Faculty of Mathematics and Computer Science, Jagiellonian University ul. Łojasiewicza 6, 30-348 Kraków, Poland
https://orcid.org/0000-0001-9416-3991 Orcid
All publications →

Faculty of Mathematics and Computer Science, Jagiellonian University ul. Łojasiewicza 6, 30-348 Kraków, Poland

Published at: 2019

Article status: Open

Licence: CC BY-NC-ND  licence icon

Percentage share of authors:

Przemysław Spurek (Author) - 20%
Przemysław Rola (Author) - 20%
Jacek Tabor (Author) - 20%
Aleksander Czechowski (Author) - 20%
Andrzej Bedychaj (Author) - 20%

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