@article{f2e0bb9b-3c17-4758-9959-651cca7c7c25,
author = {Przemysław Spurek, Przemysław Rola, Jacek Tabor, Aleksander Czechowski, Andrzej  Bedychaj },
title = {ICA based on Split Generalized Gaussian},
journal = {Schedae Informaticae},
volume = {2019},
number = {Volume 28},
year = {2019},
issn = {1732-3916},
pages = {25-47},keywords = {ICA; Split Normal distribution; skewness; kurtosis},
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.},
doi = {10.4467/20838476SI.19.002.14379},
url = {https://ejournals.eu/en/journal/schedae-informaticae/article/ica-based-on-split-generalized-gaussian}
}