A unified view on Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE)
Machine Learning
2022-05-04 v1 Neural and Evolutionary Computing
Machine Learning
Abstract
We propose a unified view on two widely used data visualization techniques: Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE). We show that they can both be derived from a common mathematical framework. Leveraging this formulation, we propose to compare SOM and SNE quantitatively on two datasets, and discuss possible avenues for future work to take advantage of both approaches.
Keywords
Cite
@article{arxiv.2205.01492,
title = {A unified view on Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE)},
author = {Thibaut Kulak and Anthony Fillion and François Blayo},
journal= {arXiv preprint arXiv:2205.01492},
year = {2022}
}