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- Title
ICA based on Split Generalized Gaussian.
- Authors
SPUREK, PRZEMYSŁAW; ROLA, PRZEMYSŁAW; TABOR, JACEK; CZECHOWSKI, ALEKSANDER; BEDYCHAJ, ANDRZEJ
- 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 ICASG 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.
- Subjects
KURTOSIS; STATISTICS; DATA analysis
- Publication
Schedae Informaticae, 2019, Vol 28, p25
- ISSN
1732-3916
- Publication type
Article
- DOI
10.4467/20838476SI.19.002.14379