A New Learning Algorithm for Blind Signal Separation
Abstract
A new on-line learning algorithm which minimizes a statistical de(cid:173) pendency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual in(cid:173) formation (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number of the sources. The Gram-Charlier expansion instead of the Edgeworth expansion is used in evaluating the MI. The natural gradient approach is used to minimize the MI. A novel activation function is proposed for the on-line learning algorithm which has an equivariant property and is easily implemented on a neural network like model. The validity of the new learning algorithm are verified by computer simulations.
Cite
Text
Amari et al. "A New Learning Algorithm for Blind Signal Separation." Neural Information Processing Systems, 1995.Markdown
[Amari et al. "A New Learning Algorithm for Blind Signal Separation." Neural Information Processing Systems, 1995.](https://mlanthology.org/neurips/1995/amari1995neurips-new/)BibTeX
@inproceedings{amari1995neurips-new,
title = {{A New Learning Algorithm for Blind Signal Separation}},
author = {Amari, Shun-ichi and Cichocki, Andrzej and Yang, Howard Hua},
booktitle = {Neural Information Processing Systems},
year = {1995},
pages = {757-763},
url = {https://mlanthology.org/neurips/1995/amari1995neurips-new/}
}