# Perplexity-free Parametric t-SNE

arXiv: https://arxiv.org/abs/2010.01359 · HTML: https://mlsanity.com/paper/2010.01359/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Navigating Perplexity: A linear relationship with the data set size in t-SNE embeddings](https://mlsanity.com/paper/2308.15513/) (2023)
- [Calibrating dimension reduction hyperparameters in the presence of noise](https://mlsanity.com/paper/2312.02946/) (2023)
- [AKRMap: Adaptive Kernel Regression for Trustworthy Visualization of Cross-Modal Embeddings](https://mlsanity.com/paper/2505.14664/) (2025)

## Abstract (arXiv)

The t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm is a ubiquitously employed dimensionality reduction (DR) method. Its non-parametric nature and impressive efficacy motivated its parametric extension. It is however bounded to a user-defined perplexity parameter, restricting its DR quality compared to recently developed multi-scale perplexity-free approaches. This paper hence proposes a multi-scale parametric t-SNE scheme, relieved from the perplexity tuning and with a deep neural network implementing the mapping. It produces reliable embeddings with out-of-sample extensions, competitive with the best perplexity adjustments in terms of neighborhood preservation on multiple data sets.