Abstract
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 language | English |
|---|---|
| Title of host publication | Progress in Neural Information Processing. Proceedings of the International Conference on Neural Information Processing |
| Pages | 1211 |
| Number of pages | 1 |
| Volume | 2 |
| Publication status | Published - 1996 |