DocumentCode :
3115984
Title :
Adaptable Nonlinearity for Complex Maximization of Nongaussianity and a Fixed-Point Algorithm
Author :
Novey, Mike ; Adali, Tiilay
Author_Institution :
Univ. of Maryland Baltimore County, Baltimore, MD, USA
fYear :
2006
fDate :
6-8 Sept. 2006
Firstpage :
79
Lastpage :
84
Abstract :
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.
Keywords :
adaptive signal processing; blind source separation; optimisation; statistical analysis; adaptable nonlinearity; adaptive CMN algorithm; fixed-point algorithm; nonGaussianity complex maximization; noncircular sources blind separation; source statistics; Adaptive algorithm; Covariance matrix; Entropy; Independent component analysis; Maximum likelihood estimation; Newton method; Parameter estimation; Random variables; Shape; Statistical distributions;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on
Conference_Location :
Arlington, VA
ISSN :
1551-2541
Print_ISBN :
1-4244-0656-0
Electronic_ISBN :
1551-2541
Type :
conf
DOI :
10.1109/MLSP.2006.275526
Filename :
4053625
Link To Document :
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