Perspectives and limitations of self-organizing maps in blind separation of source signals

M Herrmann, HH Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The capabilities of self-organizing maps (SOMs) in parametrizing data manifolds qualify them as candidates for blind separation algorithms. We study the virtues and problems of the SOM-based approach in a simple example. Also numerical simulations of more general cases have been performed. It shows that the performance is unquestionable in the case of a linear mixture only if the observed data are prewhitened and inhomogeneities in the input data are compensated. The algorithm is robust with respect to deviations from linearity, although may fail for complex non-linearly distorted signals. Due to computational restrictions only mixtures from a few sources can be resolved. Under certain conditions it is possible to separate more sources than sensors using a dimension-increasing map.
Original languageEnglish
Title of host publicationProgress in Neural Information Processing. Proceedings of the International Conference on Neural Information Processing
Number of pages1
Publication statusPublished - 1996


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