One-Unit Learning Rules for Independent Component Analysis
Abstract
Neural one-unit learning rules for the problem of Independent Com(cid:173) ponent Analysis (ICA) and blind source separation are introduced. In these new algorithms, every ICA neuron develops into a sepa(cid:173) rator that finds one of the independent components. The learning rules use very simple constrained Hebbianjanti-Hebbian learning in which decorrelating feedback may be added. To speed up the convergence of these stochastic gradient descent rules, a novel com(cid:173) putationally efficient fixed-point algorithm is introduced.
Cite
Text
Hyvärinen and Oja. "One-Unit Learning Rules for Independent Component Analysis." Neural Information Processing Systems, 1996.Markdown
[Hyvärinen and Oja. "One-Unit Learning Rules for Independent Component Analysis." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/hyvarinen1996neurips-oneunit/)BibTeX
@inproceedings{hyvarinen1996neurips-oneunit,
title = {{One-Unit Learning Rules for Independent Component Analysis}},
author = {Hyvärinen, Aapo and Oja, Erkki},
booktitle = {Neural Information Processing Systems},
year = {1996},
pages = {480-486},
url = {https://mlanthology.org/neurips/1996/hyvarinen1996neurips-oneunit/}
}