nips nips2001 nips2001-71 nips2001-71-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Frank C. Meinecke, Andreas Ziehe, Motoaki Kawanabe, Klaus-Robert Müller
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 magneto encephalography (MEG) experiment underlines the usefulness of our approach. 1
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