Adaptive blind separation of independent sources: a deflation approach
Signal Processing
The nature of statistical learning theory
The nature of statistical learning theory
A fast fixed-point algorithm for independent component analysis
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Dependence, correlation and Gaussianity in independent component analysis
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
New criteria for blind deconvolution of nonminimum phase systems (channels)
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
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Many algorithms for independent component analysis (ICA) and blind source separation (BSS) can be considered particular instances of a criterion based on the sum of two terms: C(Y), which expresses the decorrelation of the components and G(Y), which measures their non-Gaussianity. Within this framework, the popular FastICA algorithm can be regarded as a technique that keeps C(Y)=0 by first enforcing the whiteness of Y. Because of this constraint, the standard version of FastICA employs the sample-fourth moment as G(Y), instead of the sample-fourth cumulant. Our work analyzes some of the estimation errors introduced by the use of finite date sets in such a higher-order statistics (HOS) contrast and compares FastICA with an alternative version based on the sample-fourth cumulant, which is shown for different probability distributions having a lower variance in the generalization error in the case in which no whitening is performed, e.g. when orthonormal mixing of sources is present.