Non-Euclidean Self-Organizing Maps
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).
Cite
Text
Celinska-Kopczynska and Kopczynski. "Non-Euclidean Self-Organizing Maps." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/269Markdown
[Celinska-Kopczynska and Kopczynski. "Non-Euclidean Self-Organizing Maps." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/celinskakopczynska2022ijcai-non/) doi:10.24963/IJCAI.2022/269BibTeX
@inproceedings{celinskakopczynska2022ijcai-non,
title = {{Non-Euclidean Self-Organizing Maps}},
author = {Celinska-Kopczynska, Dorota and Kopczynski, Eryk},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {1938-1944},
doi = {10.24963/IJCAI.2022/269},
url = {https://mlanthology.org/ijcai/2022/celinskakopczynska2022ijcai-non/}
}