# ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE

arXiv: https://arxiv.org/abs/2205.11720 · HTML: https://mlsanity.com/paper/2205.11720/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 6 of 6)

- [Visualizing Large-scale and High-dimensional Data](https://mlsanity.com/paper/1602.00370/) (2016)
- [Automatic Selection of t-SNE Perplexity](https://mlsanity.com/paper/1708.03229/) (2017)
- [Multi-view Data Visualisation via Manifold Learning](https://mlsanity.com/paper/2101.06763/) (2021)
- [Multidimensional Scaling on Multiple Input Distance Matrices](https://mlsanity.com/paper/1605.00286/) (2016)
- [Multi-Perspective, Simultaneous Embedding](https://mlsanity.com/paper/1909.06485/) (2019)
- [Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey](https://mlsanity.com/paper/2009.10301/) (2020)

## Built on by (most cited first, 3 of 3)

- [Stop Misusing t-SNE and UMAP for Visual Analytics](https://mlsanity.com/paper/2506.08725/) (2025)
- ["Normalized Stress" is Not Normalized: How to Interpret Stress Correctly](https://mlsanity.com/paper/2408.07724/) (2024)
- [How Scale Breaks "Normalized Stress" and KL Divergence: Rethinking Quality Metrics](https://mlsanity.com/paper/2510.08660/) (2025)

## Abstract (arXiv)

When visualizing a high-dimensional dataset, dimension reduction techniques are commonly employed which provide a single 2-dimensional view of the data. We describe ENS-t-SNE: an algorithm for Embedding Neighborhoods Simultaneously that generalizes the t-Stochastic Neighborhood Embedding approach. By using different viewpoints in ENS-t-SNE's 3D embedding, one can visualize different types of clusters within the same high-dimensional dataset. This enables the viewer to see and keep track of the different types of clusters, which is harder to do when providing multiple 2D embeddings, where corresponding points cannot be easily identified. We illustrate the utility of ENS-t-SNE with real-world applications and provide an extensive quantitative evaluation with datasets of different types and sizes.