Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
High-order contrasts for independent component analysis
Neural Computation
Blind source separation via the second characteristic function
Signal Processing
Adaptive Score Functions for Maximum Likelihood ICA
Journal of VLSI Signal Processing Systems
General approach to blind source separation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Independent component analysis and (simultaneous) third-ordertensor diagonalization
IEEE Transactions on Signal Processing
A generalization of joint-diagonalization criteria for sourceseparation
IEEE Transactions on Signal Processing
Fast kernel-based independent component analysis
IEEE Transactions on Signal Processing
Noniterative map reconstruction using sparse matrix representations
IEEE Transactions on Image Processing
Fast kernel density independent component analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Multiple kernel learning with gaussianity measures
Neural Computation
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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.