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Adaptable Nonlinearity for Complex Maximization of Nongaussianity and a Fixed-Point Algorithm

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2 Author(s)
Novey, M. ; Univ. of Maryland Baltimore County, Baltimore, MD, USA ; Adali, T.

Complex maximization of nonGaussianity (CMN) has been shown to provide reliable separation of both circular and non-circular sources using a class of complex functions in the non-linearity. In this paper, we derive a fixed-point algorithm for blind separation of noncircular sources using CMN. We also introduce the adaptive CMN (A-CMN) algorithm that provides significant performance improvement by adapting the nonlinearity to the source distribution. The ability of A-CMN to adapt to a wide range of source statistics is demonstrated by simulation results.

Published in:

Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on

Date of Conference:

6-8 Sept. 2006