On KDE-based Brushing in Scatterplots and how it Compares to CNN-based Brushing

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dc.contributor.authorFan, Chaoranen_US
dc.contributor.authorHauser, Helwigen_US
dc.contributor.editorArchambault, Daniel and Nabney, Ian and Peltonen, Jaakkoen_US
dc.date.accessioned2019-06-02T18:23:43Z
dc.date.available2019-06-02T18:23:43Z
dc.date.issued2019
dc.description.abstractIn this paper, we investigate to which degree the human should be involved into the model design and how good the empirical model can be with more careful design. To find out, we extended our previously published Mahalanobis brush (the best current empirical model in terms of accuracy for brushing points in a scatterplot) by further incorporating the data distribution information that is captured by the kernel density estimation (KDE). Based on this work, we then include a short discussion between the empirical model, designed in detail by an expert and the deep learning-based model that is learned from user data directly.en_US
dc.description.sectionheadersPapers
dc.description.seriesinformationMachine Learning Methods in Visualisation for Big Data
dc.identifier.doi10.2312/mlvis.20191157
dc.identifier.isbn978-3-03868-089-5
dc.identifier.pages1-5
dc.identifier.urihttps://doi.org/10.2312/mlvis.20191157
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/mlvis20191157
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectInteraction techniques
dc.subjectComputing methodologies
dc.subjectOptimization algorithms"
dc.titleOn KDE-based Brushing in Scatterplots and how it Compares to CNN-based Brushingen_US
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Machine Learning Methods in Visualisation for Big Data 2019