Learning factorial codes by predictability minimization
Neural Computation
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Information-theoretic approach to blind separation of sources in non-linear mixture
Signal Processing - Special issue on neural networks
Entropy Optimization - Application to Blind Source Separation
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Linear and nonlinear ICA based on mutual information: the MISEP method
Signal Processing - Special issue on independent components analysis and beyond
MISEP - Linear and nonlinear ICA based on mutual information
The Journal of Machine Learning Research
Topographic Independent Component Analysis
Neural Computation
Nonlinear blind source separation using kernels
IEEE Transactions on Neural Networks
Misep—linear and nonlinear ICA based on mutual information
The Journal of Machine Learning Research
Linear and nonlinear ICA based on mutual information: the MISEP method
Signal Processing - Special issue on independent components analysis and beyond
MISEP - Linear and nonlinear ICA based on mutual information
The Journal of Machine Learning Research
Independent Slow Feature Analysis and Nonlinear Blind Source Separation
Neural Computation
MISEP Method for Postnonlinear Blind Source Separation
Neural Computation
An extension of MISEP for post-nonlinear-linear mixture separation
IEEE Transactions on Circuits and Systems II: Express Briefs
Research article: Estimating sufficient statistics in co-evolutionary analysis by mutual information
Computational Biology and Chemistry
Mutual Information Based Approach for Nonnegative Independent Component Analysis
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Post-nonlinear blind source separation using neural networks with sandwiched structure
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
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MISEP is a method for linear and nonlinear ICA, that is able to handle a large variety of situations. It is an extension of the well known INFOMAX method, in two directions: (1) handling of nonlinear mixtures, and (2) learning the nonlinearities to be used at the outputs. The method can therefore separate linear and nonlinear mixtures of components with a wide range of statistical distributions. This paper presents the basis of the MISEP method, as well as experimental results obtained with it. New results show the applicability of the method to mixtures of up to 10 sources, and suggest that its performance scales relatively well with the dimensionality of the problem. The results also show that, although the nonlinear blind source separation problem is ill-posed, the use of regularization allows the problem to be solved when the mixture is not too strongly nonlinear.