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This paper presents a novel unsupervised learning paradigm for feature extraction in linear networks under the constraint that no information distortion occurs in the input-output map. Independent feature extraction is then accomplished by minimizing an appropriate information theory based measure of the statistical dependence between the output components. In the case of Gaussian input distribution,...
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