ICA based on Split Generalized Gaussian
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Publication date: 2019
Schedae Informaticae, 2019, Volume 28, pp. 25 - 47
https://doi.org/10.4467/20838476SI.19.002.14379Authors
ICA based on Split Generalized Gaussian
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, Volume 28, pp. 25 - 47
Article type: Original article
Titles:
ICA based on Split Generalized Gaussian
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
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
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English