Online Independent Component Analysis with Local Learning Rate Adaptation
Abstract
Stochastic meta-descent (SMD) is a new technique for online adap(cid:173) tation of local learning rates in arbitrary twice-differentiable sys(cid:173) tems. Like matrix momentum it uses full second-order information while retaining O(n) computational complexity by exploiting the efficient computation of Hessian-vector products. Here we apply SMD to independent component analysis, and employ the result(cid:173) ing algorithm for the blind separation of time-varying mixtures. By matching individual learning rates to the rate of change in each source signal's mixture coefficients, our technique is capable of si(cid:173) multaneously tracking sources that move at very different, a priori unknown speeds.
Cite
Text
Schraudolph and Giannakopoulos. "Online Independent Component Analysis with Local Learning Rate Adaptation." Neural Information Processing Systems, 1999.Markdown
[Schraudolph and Giannakopoulos. "Online Independent Component Analysis with Local Learning Rate Adaptation." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/schraudolph1999neurips-online/)BibTeX
@inproceedings{schraudolph1999neurips-online,
title = {{Online Independent Component Analysis with Local Learning Rate Adaptation}},
author = {Schraudolph, Nicol N. and Giannakopoulos, Xavier},
booktitle = {Neural Information Processing Systems},
year = {1999},
pages = {789-795},
url = {https://mlanthology.org/neurips/1999/schraudolph1999neurips-online/}
}