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Complex Independent Component Analysis by Entropy Bound Minimization

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2 Author(s)
Xi-Lin Li ; Dept. of Comput. Sci. & Electr. Eng., Univ. of Maryland, Baltimore County, Baltimore, MD, USA ; Adali, T.

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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Circuits and Systems I: Regular Papers, IEEE Transactions on  (Volume:57 ,  Issue: 7 )