ICA by Maximization of Nongaussianity using Complex Functions
- 11 October 2006
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
We use complex, hence analytic, functions to achieve independent component analysis (ICA) by maximization of nonGaussianity and introduce the complex maximization of nonGaussianity (CMN) algorithm. We show that CMN converges to the principal component of the source distribution and that the algorithm provides robust performance for both circular and non-circular sourcesKeywords
This publication has 8 references indexed in Scilit:
- Independent component analysis by complex nonlinearitiesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Complex infomax: convergence and approximation of infomax with complex nonlinearitiesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued SignalsInternational Journal of Neural Systems, 2000
- Blind separation of convolved mixtures in the frequency domainNeurocomputing, 1998
- Independent component analysis by general nonlinear Hebbian-like learning rulesSignal Processing, 1998
- An Information-Maximization Approach to Blind Separation and Blind DeconvolutionNeural Computation, 1995
- On circularityIEEE Transactions on Signal Processing, 1994
- Blind beamforming for non-gaussian signalsIEE Proceedings F Radar and Signal Processing, 1993