Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.24311 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916782636269568 |
|---|---|
| author | Gu, Yi |
| author_facet | Gu, Yi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Equilibrium Distribution for t-Distributed Stochastic Neighbor Embedding with Generalized Kernels Gu, Yi Machine Learning Probability Statistics Theory 60 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. |
| title | Equilibrium Distribution for t-Distributed Stochastic Neighbor Embedding with Generalized Kernels |
| topic | Machine Learning Probability Statistics Theory 60 |
| url | https://arxiv.org/abs/2505.24311 |