ICA based on Split Generalized Gaussian
Abstract
Independent Component Analysis (ICA) - one of the basic tools in data analysis - aims to find a coordinate system in which the components of the data are independent. Most popular ICA methods use kurtosis as a metric of 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. \end{abstract}
- Publication:
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arXiv e-prints
- Pub Date:
- February 2018
- DOI:
- 10.48550/arXiv.1802.05550
- arXiv:
- arXiv:1802.05550
- Bibcode:
- 2018arXiv180205550S
- Keywords:
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- Statistics - Machine Learning
- E-Print:
- arXiv admin note: substantial text overlap with arXiv:1701.09160