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

• URL: http://arxiv.org/abs/2306.00357v1
• Date: Thu, 1 Jun 2023 05:36:22 GMT
• Title: Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization
• Authors: Yin-Ting Liao, Hengrui Luo, Anna Ma
• Abstract summary: We introduce an efficient and robust auto-tuning framework for hyper parameter selection in dimension reduction (DR) algorithms. Our approach enables efficient hyper parameter selection with multi-objective trade-offs and allows us to perform data-driven analysis. We evaluate our results on various synthetic and real-world datasets using multiple quality metrics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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