HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters

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dc.contributor.authorAppleby, Gabrielen_US
dc.contributor.authorEspadoto, Mateusen_US
dc.contributor.authorChen, Ruien_US
dc.contributor.authorGoree, Samuelen_US
dc.contributor.authorTelea, Alexandru C.en_US
dc.contributor.authorAnderson, Erik W.en_US
dc.contributor.authorChang, Remcoen_US
dc.contributor.editorBorgo, Ritaen_US
dc.contributor.editorMarai, G. Elisabetaen_US
dc.contributor.editorSchreck, Tobiasen_US
dc.date.accessioned2022-06-03T06:05:57Z
dc.date.available2022-06-03T06:05:57Z
dc.date.issued2022
dc.description.abstractProjection 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 values is computationally intensive and unintuitive due to the stochastic nature of such 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. A HyperNP model can be trained on a fraction of the total data instances and hyperparameter configurations that one would like to investigate and can compute projections for new data and hyperparameters at interactive speeds. HyperNP models are compact in size and fast to compute, thus allowing them to be embedded in lightweight visualization systems. We evaluate the performance of HyperNP across three datasets in terms of performance and speed. The results suggest that HyperNP models are accurate, scalable, interactive, and appropriate for use in real-world settings.en_US
dc.description.number3
dc.description.sectionheadersWorkflows and Parameters
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume41
dc.identifier.doi10.1111/cgf.14531
dc.identifier.issn1467-8659
dc.identifier.pages169-181
dc.identifier.pages13 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14531
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14531
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Human-centered computing --> Visualization techniques
dc.subjectHuman centered computing
dc.subjectVisualization techniques
dc.titleHyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparametersen_US
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