HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters

TL;DR

HyperNP is a neural network-based approach enabling real-time, interactive exploration of hyperparameters in high-dimensional data projection algorithms like t-SNE and UMAP, significantly improving usability and efficiency.

Contribution

It introduces HyperNP, a scalable neural network method that approximates projections for rapid hyperparameter tuning in visualization tasks.

Findings

HyperNP achieves accurate projections with minimal data training.

It enables real-time hyperparameter exploration in web-based visualization systems.

HyperNP is scalable and suitable for large datasets.

Abstract

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.