Hyvarinen, AJ;
Morioka, H;
(2017)
Nonlinear ICA of temporally dependent stationary sources.
In:
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics.
Proceedings of Machine Learning Research: Fort Lauderdale, FL, USA.
Preview |
Text
AISTATS2017.pdf - Published Version Download (827kB) | Preview |
Abstract
We develop a nonlinear generalization of independent component analysis (ICA) or blind source separation, based on temporal dependencies (e.g. autocorrelations). We introduce a nonlinear generative model where the independent sources are assumed to be temporally dependent, non-Gaussian, and stationary, and we observe arbitrarily nonlinear mixtures of them. We develop a method for estimating the model (i.e. separating the sources) based on logistic regression in a neural network which learns to discriminate between a short temporal window of the data vs. a temporal window of temporally permuted data. We prove that the method estimates the sources for general smooth mixing nonlinearities, assuming the sources have sufficiently strong temporal dependencies, and these dependencies are in a certain way different from dependencies found in Gaussian processes. For Gaussian (and similar) sources, the method estimates the nonlinear part of the mixing. We thus provide the first rigorous and general proof of identifiability of nonlinear ICA for temporally dependent sources, together with a practical method for its estimation.
| Type: | Proceedings paper |
|---|---|
| Title: | Nonlinear ICA of temporally dependent stationary sources |
| Event: | 20th International Conference on Artificial Intelligence and Statistics |
| Location: | Fort Lauderdale, Florida, USA |
| Dates: | 20 April 2017 - 22 April 2017 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://proceedings.mlr.press/v54/hyvarinen17a.html |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1547625 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
