  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.
