# Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization

arXiv: https://arxiv.org/abs/2306.00357 · HTML: https://mlsanity.com/paper/2306.00357/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 55 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 14 of 14)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Freeze-Thaw Bayesian Optimization](https://mlsanity.com/paper/1406.3896/) (2014)
- [Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data](https://mlsanity.com/paper/2105.07536/) (2021)
- [Attraction-Repulsion Spectrum in Neighbor Embeddings](https://mlsanity.com/paper/2007.08902/) (2020)
- [A Statistical Perspective on Algorithmic Leveraging](https://mlsanity.com/paper/1306.5362/) (2013)
- [Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software](https://mlsanity.com/paper/2302.11474/) (2023)
- [Automatic Selection of t-SNE Perplexity](https://mlsanity.com/paper/1708.03229/) (2017)
- [Stationary Diffusion State Neural Estimation for Multiview Clustering](https://mlsanity.com/paper/2112.01334/) (2021)
- [Non-smooth Bayesian Optimization in Tuning Problems](https://mlsanity.com/paper/2109.07563/) (2021)
- [An Empirical Evaluation of the t-SNE Algorithm for Data Visualization in Structural Engineering](https://mlsanity.com/paper/2109.08795/) (2021)
- [A Distance-preserving Matrix Sketch](https://mlsanity.com/paper/2009.03979/) (2020)
- [Spherical Rotation Dimension Reduction with Geometric Loss Functions](https://mlsanity.com/paper/2204.10975/) (2022)
- [AVIDA: Alternating method for Visualizing and Integrating Data](https://mlsanity.com/paper/2206.00135/) (2022)

## Built on by (most cited first, 1 of 1)

- [MEDAL: Manifold Embedding Distillation via Autoencoder Learning](https://mlsanity.com/paper/2605.24244/) (2026)

## Abstract (arXiv)

We introduce an efficient and robust auto-tuning framework for hyperparameter selection in dimension reduction (DR) algorithms, focusing on large-scale datasets and arbitrary performance metrics. By leveraging Bayesian optimization (BO) with a surrogate model, our approach enables efficient hyperparameter selection with multi-objective trade-offs and allows us to perform data-driven sensitivity analysis. By incorporating normalization and subsampling, the proposed framework demonstrates versatility and efficiency, as shown in applications to visualization techniques such as t-SNE and UMAP. We evaluate our results on various synthetic and real-world datasets using multiple quality metrics, providing a robust and efficient solution for hyperparameter selection in DR algorithms.