Natural gradient learning for over- and under-complete bases in ICA
Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Linear and nonlinear ICA based on mutual information: the MISEP method
Signal Processing - Special issue on independent components analysis and beyond
MISEP Method for Postnonlinear Blind Source Separation
Neural Computation
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
A generic framework for blind source separation in structurednonlinear models
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Dual multivariate auto-regressive modeling in state space for temporal signal separation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Depth estimation of face images based on the constrained ICA model
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
An eigengene-based classifier committee learning algorithm for tumor classification
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
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Mutual information separation (MISEP) is a versatile independent component analysis (ICA) algorithm that can be used to handle linear and nonlinear mixtures. By incorporating the a priori information of mixtures, an extended MISEP method is proposed in this brief to recover the source signals from the post-nonlinear-linear (PNL-L) mixtures. One group of multilayer perceptrons and two linear networks are used as the unmixing system, and another group of multilayer perceptrons is used as the auxiliary network. The learning algorithm of the system parameters is obtained by maximizing the output entropy with the gradient ascent method. Experimental results demonstrate that the proposed method is effective and efficient for PNL-L mixture separation.