Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization
This provides a solution for researchers and practitioners needing robust hyperparameter tuning in visualization tasks, though it is incremental as it builds on existing Bayesian optimization methods.
The paper tackles the problem of hyperparameter selection in dimension reduction algorithms for visualization by introducing an efficient and robust auto-tuning framework using Bayesian optimization, achieving versatility and efficiency as demonstrated on datasets like t-SNE and UMAP.
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.