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

Yin-Ting Liao; Hengrui Luo; Anna Ma

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arXiv · 2306.00357

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

Yin-Ting Liao·Hengrui Luo·Anna Ma

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Abstract

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.

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Yin-Ting Liao, Hengrui Luo, Anna Ma. 2023-06-01. Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization. https://arxiv.org/abs/2306.00357

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