On model identifiability in analytic postnonlinear ICA

Authors:
F. J. Theis;P. Gruber
Affiliations:
Institute of Biophysics, University of Regensburg, D-93040 Regensburg, Germany;Institute of Biophysics, University of Regensburg, D-93040 Regensburg, Germany
Venue:
Neurocomputing
Year:
2005

Citing 9
Cited 1

Space or time adaptive signal processing by neural network models

AIP Conference Proceedings 151 on Neural Networks for Computing
Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
An information-maximization approach to blind separation and blind deconvolution

Neural Computation
A fast fixed-point algorithm for independent component analysis

Neural Computation
Linear geometric ICA: fundamentals and algorithms

Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Uniqueness of complex and multidimensional independent component analysis

Signal Processing
A new concept for separability problems in blind source separation

Neural Computation
Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation

The Journal of Machine Learning Research

Comment on "Blind source separation based on endpoint estimation with applications to the MLSP 2006 data competition"

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

An important aspect of successfully analyzing data with blind source separation is to know the indeterminacies of the problem, that is how the separating model is related to the original mixing model. If linear independent component analysis (ICA) is used, it is well-known that the mixing matrix can be found in principle, but for more general settings not many results exist. In this work, only considering random variables with bounded densities, we prove identifiability of the postnonlinear mixing model with analytic nonlinearities and calculate its indeterminacies. A simulation confirms these theoretical findings.