Abstract
The goal of blind source separation (BSS) is to recover independent sources given only sensor observations that are linear mixtures of independent source signals. The term blind indicates that both the source signals and the way the signals were mixed are unknown. Independent Component Analysis (ICA) is a method for solving the blind source separation problem. It is a way to find a linear coordinate system (the unmixing system) such that the resulting signals are as statistically independent from each other as possible. In contrast to correlation-based transformations such as Principal Component Analysis (PCA), ICA not only decorrelates the signals (2nd-order statistics) but also reduces higher-order statistical dependencies.
The world beyond second-order statistics
Anthony Bell
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Notes
as detailed in section 4 of Bell and Sejnowski (1995)
see eqs. 40 and 41 in their paper.
Symmetric bimodal densities considered in this paper are sub-Gaussian, however this is not always the case.
The presented estimation theory is related to the semiparametrical statistical approach by Amari and Cardoso (1997) and the stability analysis of adaptive blind source separation (Amari et al., 1997a)
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© 1998 Springer Science+Business Media Dordrecht
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Lee, TW. (1998). Independent Component Analysis. In: Independent Component Analysis. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2851-4_2
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DOI: https://doi.org/10.1007/978-1-4757-2851-4_2
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