The identification of nonlinear biological systems: LNL cascade models
Biological Cybernetics
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
Feature extraction through LOCOCODE
Neural Computation
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
The Journal of Machine Learning Research
A post nonlinear geometric algorithm for independent component analysis
Digital Signal Processing
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
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A new geometric approach for nonlinear independent component analysis (ICA) is presented in this paper. Nonlinear environment is modeled by the popular post nonlinear (PNL) scheme. To eliminate the nonlinearity in the observed signals, a novel linearizing method named as geometric post nonlinear ICA (gpICA) is introduced. Thereafter, a basic linear ICA is applied on these linearized signals to estimate the unknown sources. The proposed method is motivated by the fact that in a multidimensional space, a nonlinear mixture is represented by a nonlinear surface while a linear mixture is represented by a plane, a special form of the surface. Therefore, by geometrically transforming the surface representing a nonlinear mixture into a plane, the mixture can be linearized. Through simulations on different data sets, superior performance of gpICA algorithm has been shown with respect to other algorithms.