Equilibrium Distribution for t-Distributed Stochastic Neighbor Embedding with Generalized Kernels
Machine Learning
2025-06-10 v2 Machine Learning
Probability
Statistics Theory
Statistics Theory
Abstract
T-distributed stochastic neighbor embedding (t-SNE) is a well-known algorithm for visualizing high-dimensional data by finding low-dimensional representations. In this paper, we study the convergence of t-SNE with generalized kernels and extend the results of Auffinger and Fletcher in 2023. Our work starts by giving a concrete formulation of generalized input and output kernels. Then we prove that under certain conditions, the t-SNE algorithm converges to an equilibrium distribution for a wide range of input and output kernels as the number of data points diverges.
Keywords
Cite
@article{arxiv.2505.24311,
title = {Equilibrium Distribution for t-Distributed Stochastic Neighbor Embedding with Generalized Kernels},
author = {Yi Gu},
journal= {arXiv preprint arXiv:2505.24311},
year = {2025}
}