The identification of nonlinear biological systems: LNL cascade models
Biological Cybernetics
Information-theoretic approach to blind separation of sources in non-linear mixture
Signal Processing - Special issue on neural networks
A Maximum Likelihood Approach to Nonlinear 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
Blind image extraction from nonlinear mixtures using MLP-based ICA
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
Blind identification of LTI-ZMNL-LTI nonlinear channel models
IEEE Transactions on Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
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
Blind source separation by nonstationarity of variance: a cumulant-based approach
IEEE Transactions on Neural Networks
Nonlinear blind source separation using kernels
IEEE Transactions on Neural Networks
Nonlinear underdetermined blind signal separation using Bayesian neural network approach
Digital Signal Processing
gpICA: a novel nonlinear ICA algorithm using geometric linearization
EURASIP Journal on Applied Signal Processing
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Simple linear independent component analysis (ICA) algorithms work efficiently only in linear mixing environments. Whereas, a nonlinear ICA model, which is more complicated, would be more practical for general applications as it can work with both linear and nonlinear mixtures. In this paper, we introduce a novel method for nonlinear ICA problem. The proposed method follows the post nonlinear approach to model the mixtures, and exploits the difference between a linear mixture and a nonlinear one from their nature of distributions in a multidimensional space to develop a separation scheme. The nonlinear mixture is represented by a nonlinear surface while the linear mixture is represented by a plane. A geometric learning algorithm named as post nonlinear geometric ICA (pnGICA) is developed by geometrically transforming the nonlinear surface to a plane, i.e., to a linear mixture. Computer simulations of the algorithm provide promising performance on different data sets.