A unified view on Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE)
This work provides a theoretical unification for data visualization techniques, but it is incremental as it builds on existing methods without introducing new applications.
The paper tackled the problem of unifying Self-Organizing Maps (SOMs) and Stochastic Neighbor Embedding (SNE) by deriving them from a common mathematical framework, and it quantitatively compared them on two datasets.
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.