Abstract
A novel characteristic-function-based method for blind separation of statistically independent source signals is proposed in the independent component analysis (ICA) framework. The definition of independence may be given in terms of factorization of joint characteristic function. Three criteria for ICA are derived based on this property. These criteria always exist and two of them have desirable large sample properties. An objective function for estimating the independence criteria directly from data is proposed. An efficient algorithm using Fourier coefficients is developed for minimizing the objective function. Simulation results demonstrate that the method performs reliably even in such situations where many widely used ICA methods may fail.
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