Matrix analysis
Complex independent component analysis of frequency-domain electroencephalographic data
Neural Networks - Special issue: Neuroinformatics
EURASIP Journal on Applied Signal Processing
Complex-valued ICA based on a pair of generalized covariance matrices
Computational Statistics & Data Analysis
IEEE Transactions on Signal Processing
Widely linear estimation with complex data
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Subspace-based direction-of-arrival estimation using nonparametricstatistics
IEEE Transactions on Signal Processing
Complex random vectors and ICA models: identifiability, uniqueness, and separability
IEEE Transactions on Information Theory
Proper complex random processes with applications to information theory
IEEE Transactions on Information Theory
Complex-valued ICA based on a pair of generalized covariance matrices
Computational Statistics & Data Analysis
Physical-layer network coding over wireless fading channel
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Complex blind source extraction from noisy mixtures using second-order statistics
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Complex independent component analysis by entropy bound minimization
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Complex blind source separation via simultaneous strong uncorrelating transform
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
A general approach for robustification of ICA algorithms
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
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An extension of the whitening transformation for complex random vectors, called the generalized uncorrelating transformation (GUT), is introduced. GUT is a generalization of the strong-uncorrelating transform [J. Eriksson, V. Koivunen, Complex-valued ICA using 2nd-order statistics, in: Proceedings of the IEEE Workshop on Machine Learning for Signal Processing (MLSP'04), Sao Luis, Brazil, 2004] based upon generalized estimators of the covariance and pseudo-covariance matrix, called the scatter matrix and spatial pseudo-scatter matrix, respectively. Depending on the selected scatter and spatial pseudo-scatter matrix, GUT estimators can have largely different statistical properties. Special emphasis is put on robust GUT estimators. We show that GUT is a separating matrix estimator for complex-valued independent component analysis (ICA) when at most one source random variable possess circularly symmetric distribution and sources do not have identical distribution. In the context of ICA, our approach is computationally attractive as it is based on straightforward matrix computations. Simulations and examples are used to confirm reliable performance of our method.