Multilayer feedforward networks are universal approximators
Neural Networks
Elements of information theory
Elements of information theory
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
Natural gradient works efficiently in learning
Neural Computation
Neural Computation
Independent component analysis: algorithms and applications
Neural Networks
The Journal of Machine Learning Research
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixture of sources based on orderstatistics
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
Gaussianization Based Approach for Post-Nonlinear Underdetermined BSS with Delays
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
PCA Gaussianization for image processing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Extensions of ICA for causality discovery in the hong kong stock market
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
| Hi-index | 0.00 |
The linear mixture model has been investigated in most articles tackling the problem of blind source separation. Recently, several articles have addressed a more complex model: blind source separation (BSS) of postnonlinear (PNL) mixtures. These mixtures are assumed to be generated by applying an unknown invertible nonlinear distortion to linear instantaneous mixtures of some independent sources. The gaussianization technique for BSS of PNL mixtures emerged based on the assumption that the distribution of the linear mixture of independent sources is gaussian. In this letter, we review the gaussianization method and then extend it to apply to PNL mixture in which the linear mixture is close to gaussian. Our proposed method approximates the linear mixture using the CornishFisher expansion. We choose the mutual information as the independence measurement to develop a learning algorithm to separate PNL mixtures. This method provides better applicability and accuracy. We then discuss the sufficient condition for the method to be valid. The characteristics of the nonlinearity do not affect the performance of this method. With only a few parameters to tune, our algorithm has a comparatively low computation. Finally, we present experiments to illustrate the efficiency of our method.