Kernel-based nonlinear blind source separation
Neural Computation
MISEP - Linear and nonlinear ICA based on mutual information
The Journal of Machine Learning Research
Separating a Real-Life Nonlinear Image Mixture
The Journal of Machine Learning Research
A Linear Non-Gaussian Acyclic Model for Causal Discovery
The Journal of Machine Learning Research
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Nonlinear blind source separation using a radial basis function network
IEEE Transactions on Neural Networks
Integrating Nonlinear Independent Component Analysis and Neural Network in Stock Price Prediction
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Kernel-based nonlinear independent component analysis
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Expert Systems with Applications: An International Journal
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Nonlinear ICA may not result in nonlinear blind source separation, since solutions to nonlinear ICA are highly non-unique. In practice, the nonlinearity in the data generation procedure is usually not strong. Thus it is reasonable to select the solution with the mixing procedure close to linear. In this paper we propose to solve nonlinear ICA with the "minimal nonlinear distortion" principle. This is achieved by incorporating a regularization term to minimize the mean square error between the mixing mapping and the best-fitting linear one. As an application, the proposed method helps to identify linear, non-Gaussian, and acyclic causal models when mild nonlinearity exists in the data generation procedure. Using this method to separate daily returns of a set of stocks, we successfully identify their linear causal relations. The resulting causal relations give some interesting insights into the stock market.