Title: Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization Show Chinese title

Title: 降维可视化中高效稳健的超参数贝叶斯选择

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. Show Chinese abstract

Abstract: 我们引入了一个高效且稳健的自动调优框架，用于降维（DR）算法中的超参数选择，重点针对大规模数据集和任意性能指标。 通过利用带有代理模型的贝叶斯优化（BO），我们的方法能够实现高效的超参数选择，并支持多目标权衡，使我们能够进行数据驱动的敏感性分析。 通过引入归一化和子采样，所提出的框架展示了通用性和效率，如在t-SNE和UMAP等可视化技术中的应用所示。 我们使用多种质量指标在各种合成和现实数据集上评估了我们的结果，为DR算法中的超参数选择提供了一个稳健且高效解决方案。