# A unified view on Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE)

arXiv: https://arxiv.org/abs/2205.01492 · HTML: https://mlsanity.com/paper/2205.01492/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 1 of 1)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)

## Abstract (arXiv)

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.