Journal
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
Volume 57, Issue 7, Pages 1417-1430Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSI.2010.2046207
Keywords
Complex optimization; complex random variable; differential entropy; independent component analysis (ICA); neural networks; principle of maximum entropy
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Funding
- National Science Foundation [NSF-CCF 0635129, NSF-IIS 0612076]
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We first present a new (differential) entropy estimator for complex random variables by approximating the entropy estimate using a numerically computed maximum entropy bound. The associated maximum entropy distributions belong to the class of weighted linear combinations and elliptical distributions, and together, they provide a rich array of bivariate distributions for density matching. Next, we introduce a new complex independent component analysis (ICA) algorithm, complex ICA by entropy-bound minimization (complex ICA-EBM), using this new entropy estimator and a line search optimization procedure. We present simulation results to demonstrate the superior separation performance and computational efficiency of complex ICA-EBM in separation of complex sources that come from a wide range of bivariate distributions.
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