HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters
This addresses the challenge for data analysts and visualization practitioners who need efficient hyperparameter tuning in projection-based visualizations, representing an incremental improvement by applying neural approximations to an existing bottleneck.
The paper tackles the problem of computationally intensive and unintuitive hyperparameter tuning for projection algorithms like t-SNE or UMAP by proposing HyperNP, a scalable neural network approximation method that enables real-time interactive exploration, achieving accurate and fast performance across three datasets.
Projection algorithms such as t-SNE or UMAP are useful for the visualization of high dimensional data, but depend on hyperparameters which must be tuned carefully. Unfortunately, iteratively recomputing projections to find the optimal hyperparameter value is computationally intensive and unintuitive due to the stochastic nature of these methods. In this paper we propose HyperNP, a scalable method that allows for real-time interactive hyperparameter exploration of projection methods by training neural network approximations. HyperNP can be trained on a fraction of the total data instances and hyperparameter configurations and can compute projections for new data and hyperparameters at interactive speeds. HyperNP is compact in size and fast to compute, thus allowing it to be embedded in lightweight visualization systems such as web browsers. We evaluate the performance of the HyperNP across three datasets in terms of performance and speed. The results suggest that HyperNP is accurate, scalable, interactive, and appropriate for use in real-world settings.