mlsanityData to 14 Oct 2026

Some Theoretical Limitations of t-SNE

arXiv 2604.13295 ↗First posted 14 Apr 2026cs.LG math.PR stat.MLcited by 1

Not on the map. Why?

Abstract (arXiv)

t-SNE has gained popularity as a dimension reduction technique, especially for visualizing data. It is well-known that all dimension reduction techniques may lose important features of the data. We provide a mathematical framework for understanding this loss for t-SNE by establishing a number of results in different scenarios showing how important features of data are lost by using t-SNE.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 8 references that are in the map. First posted on arXiv 14 Apr 2026.

Builds on

3 of 8 references.

References · 3

  1. 2021Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Datacited by 55
  2. 2017Clustering with t-SNE, provablycited by 51
  3. 2018An Analysis of the t-SNE Algorithm for Data Visualizationcited by 49

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026On the Abundance of Critical Points of the t-SNE Energycited by 0