Non-Euclidean Self-Organizing Maps

Dorota Celińska-Kopczyńska; Eryk Kopczyński

IJCAI 2022poster1 citations

Non-Euclidean Self-Organizing Maps

Dorota Celińska-Kopczyńska, Eryk Kopczyński

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).

Data Mining: Exploratory Data MiningData Mining: Data VisualisationData Mining: OtherMachine Learning: Feature Extraction, Selection and Dimensionality Reduction

BibTeX

@inproceedings{ijcai2022p269,
  title     = {Non-Euclidean Self-Organizing Maps},
  author    = {Celińska-Kopczyńska, Dorota and Kopczyński, Eryk},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {1938--1944},
  year      = {2022},
  month     = {7},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2022/269},
  url       = {https://doi.org/10.24963/ijcai.2022/269},
}