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

TL;DR

This paper presents a unified mathematical framework for Self-Organizing Maps and Stochastic Neighbor Embedding, enabling direct comparison and potential integration of these popular data visualization techniques.

Contribution

It introduces a common theoretical basis for SOMs and SNE, facilitating their comparison and inspiring future hybrid approaches.

Findings

Quantitative comparison of SOM and SNE on two datasets.

Discussion of advantages and limitations of both methods.

Proposes future research directions leveraging the unified framework.

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.