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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.19.002.14379Authors

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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.

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** Information:**
Schedae Informaticae,
2019,
pp. 25 - 47

** Article type:**
Original research article

**Titles:**

English:

ICA based on Split Generalized Gaussian

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

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

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

Delft University of Technology, Delft, The Netherlands

** Published at:**
2019

** Article status:**
Open

** Licence:** CC BY-NC-ND

** 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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