American Journal of Mathematical and Management Sciences - Special issue: modern digital simulation methodology, II
Natural gradient works efficiently in learning
Neural Computation
Neural Computation
The Fixed-Point Algorithm and Maximum Likelihood Estimation forIndependent Component Analysis
Neural Processing Letters
Blind separation of instantaneous mixture of sources via anindependent component analysis
IEEE Transactions on Signal Processing
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Blind source separation-semiparametric statistical approach
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Blind underdetermined mixture identification by joint canonical decomposition of HO cumulants
IEEE Transactions on Signal Processing
A toolbox for model-free analysis of fMRI data
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity
The Journal of Machine Learning Research
Expert Systems with Applications: An International Journal
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
An efficient independent component analysis algorithm for sub-gaussian sources
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
A semiparametric approach to source separation using independent component analysis
Computational Statistics & Data Analysis
Pairwise likelihood ratios for estimation of non-Gaussian structural equation models
The Journal of Machine Learning Research
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we introduce a mutual lnformation-based method for blind separation ot statistically independent source signals. The Pearson system is used as a parametric model. Starting from the definition of mutual information we show using the results by Pham (IEEE Trans. Signal Process. 44(11) (1996) 2768-2779) that the minimization of mutual information contrast leads to iterative use of score functions as estimation functions. The Pearson system allows adaptive modeling of the score functions. The characteristics of the Pearson system are studied and estimators for the parameters are derived using the method of moments. The flexibility of the Pearson system makes it possible to model wide range of source distributions including asymmetric distributions. Skewed source distributions are common in many key application areas, such as telecommunications and biomedical signal processing. We also introduce an extension of the Pearson system that can model multimodal distributions. The applicability of the Pearson system-based method is demonstrated in simulation examples, including blind equalization of GMSK signals.