Non-Euclidean Self-Organizing Maps
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Main Track. Pages 1938-1944.
https://doi.org/10.24963/ijcai.2022/269
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).
Keywords:
Data Mining: Exploratory Data Mining
Data Mining: Data Visualisation
Data Mining: Other
Machine Learning: Feature Extraction, Selection and Dimensionality Reduction