| Autor: |
SPUREK, PRZEMYSŁAW, ROLA, PRZEMYSŁAW, TABOR, JACEK, CZECHOWSKI, ALEKSANDER, BEDYCHAJ, ANDRZEJ |
| Předmět: |
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| Zdroj: |
Schedae Informaticae; 2019, Vol. 28, p25-47, 23p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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