HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters

• URL: http://arxiv.org/abs/2106.13777v1
• Date: Fri, 25 Jun 2021 17:28:14 GMT
• Title: HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters
• Authors: Gabriel Appleby, Mateus Espadoto, Rui Chen, Samuel Goree, Alexandru Telea, Erik W Anderson, Remco Chang
• Abstract summary: HyperNP is a scalable method that allows for real-time interactive exploration of projection methods by training neural network approximations. We evaluate the performance of the HyperNP across three datasets in terms of performance and speed.
• Score: 61.354362652006834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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