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Abstract:
When applying unsupervised learning techniques like ICA or
temporal decorrelation, a key question is whether the discovered
projections are reliable. In other words: can we give
error bars
or can we assess the
quality
of our separation? We use resampling methods to tackle these
questions and show experimentally that our proposed variance
estimations are strongly correlated to the separation error. We
demonstrate that this reliability estimation can be used to
choose the appropriate ICA-model, to enhance significantly the
separation performance, and, most important, to mark the
components that have a actual physical meaning. Application to
49-channel-data from an magnetoencephalography (MEG) experiment
underlines the usefulness of our approach.
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