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Subspace identification through blind source separation

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons83948

Grosse-Wentrup,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Grosse-Wentrup, M. (2006). Subspace identification through blind source separation. IEEE Signal Processing Letters, 13(2), 100-103. doi:10.1109/LSP.2005.861581.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D2EB-A
Abstract
Given a linear and instantaneous mixture model, we prove that for blind source separation (BSS) algorithms based on mutual information, only sources with non-Gaussian distribution are consistently reconstructed independent of initial conditions. This allows the identification of non-Gaussian sources and consequently the identification of signal and noise subspaces through BSS. The results are illustrated with a simple example, and the implications for a variety of signal processing applications, such as denoising and model identification, are discussed.