TY - JOUR
TI - ICA based on Split Generalized Gaussian
AU - Spurek, Przemysław
AU - Rola, Przemysław
AU - Tabor, Jacek
AU - Czechowski, Aleksander
AU - Bedychaj, Andrzej
TI - ICA based on Split Generalized Gaussian
AB - 
	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.
VL - 2019
IS - Volume 28
PY - 2019
SN - 1732-3916
C1 - 2083-8476
SP - 25
EP - 47
DO - 10.4467/20838476SI.19.002.14379
UR - https://ejournals.eu/en/journal/schedae-informaticae/article/ica-based-on-split-generalized-gaussian
KW - ICA
KW - Split Normal distribution
KW - skewness
KW - kurtosis