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Robust self-organization with M-estimators

Abstract:

Most of the work done on self-organizing maps relies on the minimization of the mean squared error. This nonrobust approach leads to poor performance in the presence of outliers. Here we consider robust M-estimators as an alternative for least squares in the context of self-organization. New learning rules are derived, so that the original Kohonen's SOFM learning rule is a particular case. Experimental results are presented which demonstrate the robustness of our method against outliers, when compared to other robust self-organizing maps.