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
A Note on Stone's Conjecture of Blind Signal Separation
Neural Computation
Nonlinear underdetermined blind signal separation using Bayesian neural network approach
Digital Signal Processing
MISEP Method for Postnonlinear Blind Source Separation
Neural Computation
Nonlinear estimation of subpixel proportion via kernel least square regression
International Journal of Remote Sensing
A PDF-Matched Modification to Stone's Measure of Predictability for Blind Source Separation
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
A post nonlinear geometric algorithm for independent component analysis
Digital Signal Processing
The generalized eigendecomposition approach to the blind source separation problem
Digital Signal Processing
On support vector regression machines with linguistic interpretation of the kernel matrix
Fuzzy Sets and Systems
Kernel-based nonlinear independent component analysis
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Research of blind images separation algorithm based on Kernel space
ICNC'09 Proceedings of the 5th international conference on Natural computation
Nonlinear nonnegative matrix factorization based on Mercer kernel construction
Pattern Recognition
Extracting post-nonlinear signal with reference
Computers and Electrical Engineering
Nonlinear adaptive blind source separation based on kernel function
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Blind multiuser detection based on kernel approximation
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Nonlinear blind source separation using hybrid neural networks
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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We derive a new method for solving nonlinear blind source separation (BSS) problems by exploiting second-order statistics in a kernel induced feature space. This paper extends a new and efficient closed-form linear algorithm to the nonlinear domain using the kernel trick originally applied in support vector machines (SVMs). This technique could likewise be applied to other linear covariance-based source separation algorithms. Experiments on realistic nonlinear mixtures of speech signals, gas multisensor data, and visual disparity data illustrate the applicability of our approach.