mlsanityData to 7 Oct 2026

t-SNE, Forceful Colorings and Mean Field Limits

arXiv 2102.13009 ↗First posted 25 Feb 2021cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

t-SNE is one of the most commonly used force-based nonlinear dimensionality reduction methods. This paper has two contributions: the first is forceful colorings, an idea that is also applicable to other force-based methods (UMAP, ForceAtlas2,...). In every equilibrium, the attractive and repulsive forces acting on a particle cancel out: however, both the size and the direction of the attractive (or repulsive) forces acting on a particle are related to its properties: the force vector can serve as an additional feature. Secondly, we analyze the case of t-SNE acting on a single homogeneous cluster (modeled by affinities coming from the adjacency matrix of a random k-regular graph); we derive a mean-field model that leads to interesting questions in classical calculus of variations. The model predicts that, in the limit, the t-SNE embedding of a single perfectly homogeneous cluster is not a point but a thin annulus of diameter ~ k-1/4 n-1/4. This is supported by numerical results. The mean field ansatz extends to other force-based dimensionality reduction methods.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 7 of its 26 references that are in the map. First posted on arXiv 25 Feb 2021.

Builds on

7 of 26 references.

References · 7

  1. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  2. 2020Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualizationcited by 170
  3. 2016Visualizing Large-scale and High-dimensional Datacited by 79
  4. 2017Efficient Algorithms for t-distributed Stochastic Neighborhood Embeddingcited by 75
  5. 2017Clustering with t-SNE, provablycited by 51
  6. 2018An Analysis of the t-SNE Algorithm for Data Visualizationcited by 49
  7. 2019Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisationscited by 22

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 5; 2025: 0; 2026: 1.

10 papers.