  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.
