Estimating the Reliability of ICA Projections
Abstract
When applying unsupervised learning techniques like ICA or tem(cid:173) poral decorrelation, a key question is whether the discovered pro(cid:173) jections are reliable. In other words: can we give error bars or can we assess the quality of our separation? We use resampling meth(cid:173) ods to tackle these questions and show experimentally that our proposed variance estimations are strongly correlated to the sepa(cid:173) ration error. We demonstrate that this reliability estimation can be used to choose the appropriate ICA-model, to enhance signifi(cid:173) cantly the separation performance, and, most important, to mark the components that have a actual physical meaning. Application to 49-channel-data from an magneto encephalography (MEG) ex(cid:173) periment underlines the usefulness of our approach.
Cite
Text
Meinecke et al. "Estimating the Reliability of ICA Projections." Neural Information Processing Systems, 2001.Markdown
[Meinecke et al. "Estimating the Reliability of ICA Projections." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/meinecke2001neurips-estimating/)BibTeX
@inproceedings{meinecke2001neurips-estimating,
title = {{Estimating the Reliability of ICA Projections}},
author = {Meinecke, Frank C. and Ziehe, Andreas and Kawanabe, Motoaki and Müller, Klaus-Robert},
booktitle = {Neural Information Processing Systems},
year = {2001},
pages = {1181-1188},
url = {https://mlanthology.org/neurips/2001/meinecke2001neurips-estimating/}
}