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Equilibrium Distribution for t-Distributed Stochastic Neighbor Embedding with Generalized Kernels

arXiv 2505.24311 ↗First posted 30 May 2025stat.ML cs.LG math.PRcited by 0

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Abstract (arXiv)

We extend the results by Auffinger and Fletcher in 2023 on the large-sample limit of t-SNE to a broader class of input and output kernels. We prove that under natural assumptions on the input distribution and the decay of the output kernel, if perplexity scales linearly with data size, the empirical measures and values of minimizers converge to an equilibrium distribution. We conclude with numerical examples comparing different kernels.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 12 references that are in the map. First posted on arXiv 30 May 2025.

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References · 5

  1. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  2. 2017Efficient Algorithms for t-distributed Stochastic Neighborhood Embeddingcited by 75
  3. 2018An Analysis of the t-SNE Algorithm for Data Visualizationcited by 49
  4. 2019Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisationscited by 22
  5. 2024Large data limits and scaling laws for tSNEcited by 4

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