Abstract
Self-organizing maps (SOM) have been obtained mainly on regular lattices, embedded in euclidean or non-euclidean spaces [1]. We present preliminar results that show SOM can be formed on non-regular lattices by giving evidence that topographic error (TE) is influenced by a few statistical parameters of the neuron lattice, such as the characteristic path length (L), the cluster coefficient (C) and the characteristic connectivity length (Lg). TE is lower not in regular lattices, but in lattices that present a particular set of statistical parameters. In an attempt to identify that set of statistical parameters, we applied mutual information function between the parameters and the TE as well as C4.5 algorithm to obtain rules that identify lattices in which SOMs show low TE.
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Neme, A., Miramontes, P. (2005). Statistical Properties of Lattices Affect Topographic Error in Self-organizing Maps. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_67
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DOI: https://doi.org/10.1007/11550822_67
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