# t-SNE Exaggerates Clusters, Provably

arXiv: https://arxiv.org/abs/2510.07746 · HTML: https://mlsanity.com/paper/2510.07746/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 17 of 17)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Visualizing Large-scale and High-dimensional Data](https://mlsanity.com/paper/1602.00370/) (2016)
- [TriMap: Large-scale Dimensionality Reduction Using Triplets](https://mlsanity.com/paper/1910.00204/) (2019)
- [Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data](https://mlsanity.com/paper/2105.07536/) (2021)
- [Clustering with t-SNE, provably](https://mlsanity.com/paper/1706.02582/) (2017)
- [An Analysis of the t-SNE Algorithm for Data Visualization](https://mlsanity.com/paper/1803.01768/) (2018)
- [Text Clustering with Large Language Model Embeddings](https://mlsanity.com/paper/2403.15112/) (2024)
- [Stochastic Neighbor Embedding under f-divergences](https://mlsanity.com/paper/1811.01247/) (2018)
- [Low-dimensional embeddings of high-dimensional data](https://mlsanity.com/paper/2508.15929/) (2025)
- [Stochastic Neighbor Embedding separates well-separated clusters](https://mlsanity.com/paper/1702.02670/) (2017)
- [Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling](https://mlsanity.com/paper/2209.08004/) (2022)
- [Convergence analysis of t-SNE as a gradient flow for point cloud on a manifold](https://mlsanity.com/paper/2401.17675/) (2024)
- [T-SNE Is Not Optimized to Reveal Clusters in Data](https://mlsanity.com/paper/2110.02573/) (2021)
- [Compressibility Barriers to Neighborhood-Preserving Data Visualizations](https://mlsanity.com/paper/2508.07119/) (2025)
- [Connection graph Laplacian methods can be made robust to noise](https://mlsanity.com/paper/1405.6231/) (2014)
- [Stochastic neighborhood embedding and the gradient flow of relative entropy](https://mlsanity.com/paper/2409.16963/) (2024)

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

- [On the continuum limit of t-SNE for data visualization](https://mlsanity.com/paper/2604.12041/) (2026)
- [On the Abundance of Critical Points of the t-SNE Energy](https://mlsanity.com/paper/2609.04379/) (2026)
- [ibUMAP: Coherent and Scalable Field Evaluation for UMAP Optimization](https://mlsanity.com/paper/2610.01445/) (2026)

## Abstract (arXiv)

Central to the widespread use of t-distributed stochastic neighbor embedding (t-SNE) is the conviction that it produces visualizations whose structure roughly matches that of the input. To the contrary, we prove that (1) the strength of the input clustering, and (2) the extremity of outlier points, cannot be reliably inferred from the t-SNE output. We demonstrate the prevalence of these failure modes in practice as well.