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.

翻译：我们提出了一种高效且鲁棒的自动调优框架，用于降维（DR）算法中的超参数选择问题，重点面向大规模数据集和任意性能指标。通过利用基于代理模型的贝叶斯优化（BO），该方法实现了考虑多目标权衡的高效超参数选择，并支持数据驱动的灵敏度分析。通过引入归一化与子采样策略，所提框架在t-SNE和UMAP等可视化技术中展现出良好的通用性与高效性。我们基于多种合成与真实数据集，采用多项质量指标对结果进行综合评估，为降维算法的超参数选择提供了稳健高效的解决方案。