Non-Euclidean Self-Organizing Maps

Dorota Celińska-Kopczyńska; Eryk Kopczyński

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arXiv · 2109.11769

Non-Euclidean Self-Organizing Maps

Dorota Celińska-Kopczyńska·Eryk Kopczyński

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Abstract

Self-Organizing Maps (SOMs, Kohonen networks) belong to neural network models of the unsupervised class. In this paper, we present the generalized setup for non-Euclidean SOMs. Most data analysts take it for granted to use some subregions of a flat space as their data model; however, by the assumption that the underlying geometry is non-Euclidean we obtain a new degree of freedom for the techniques that translate the similarities into spatial neighborhood relationships. We improve the traditional SOM algorithm by introducing topology-related extensions. Our proposition can be successfully applied to dimension reduction, clustering or finding similarities in big data (both hierarchical and non-hierarchical).

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Dorota Celińska-Kopczyńska, Eryk Kopczyński. 2021-09-24. Non-Euclidean Self-Organizing Maps. https://arxiv.org/abs/2109.11769

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