The Journal of Machine Learning Research
Three easy ways for separating nonlinear mixtures?
Signal Processing - Special issue on independent components analysis and beyond
The Journal of Machine Learning Research
An information theoretic approach to a novel nonlinear independent component analysis paradigm
Signal Processing - Special issue: Information theoretic signal processing
Independent Slow Feature Analysis and Nonlinear Blind Source Separation
Neural Computation
MISEP Method for Postnonlinear Blind Source Separation
Neural Computation
An extension of MISEP for post-nonlinear-linear mixture separation
IEEE Transactions on Circuits and Systems II: Express Briefs
A recurrent ICA approach to a novel BSS convolutive nonlinear problem
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Nonlinear blind source separation applied to a simple bijective model
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
| Hi-index | 35.68 |
This paper is concerned with blind source separation in nonlinear models. Special attention is paid to separability issues. Results show that separation is impossible in the general case. However, for specific nonlinear models, the problem becomes tractable. A generic set of parametric nonlinear mixtures is considered: This set has the Lie group structure (a group structure with continuous binary operation). In the parameter set, a definition of a relative gradient is given and is used to design both batch and stochastic algorithms. For the latter, it is shown how a proper use of the relative gradient leads to equivariant adaptive algorithms