Selecting and Sharing Multidimensional Projection Algorithms: A Practical View

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dc.contributor.authorEspadoto, Mateusen_US
dc.contributor.authorVernier, Eduardo Faccinen_US
dc.contributor.authorTelea, Alexandru C.en_US
dc.contributor.editorGillmann, Christina and Krone, Michael and Reina, Guido and Wischgoll, Thomasen_US
dc.date.accessioned2020-05-24T13:35:04Z
dc.date.available2020-05-24T13:35:04Z
dc.date.issued2020
dc.description.abstractMultidimensional Projection techniques are often used by data analysts for exploring multivariate datasets, but the task of selecting the best technique for the job is not trivial, as there are many candidates and the reasons for picking one over another are usually unclear. On the other hand, researchers developing new techniques can have a hard time comparing their new technique to existing ones and sharing their code in a way that makes it readily available for the public. In this paper, we try to address those issues systematically by analyzing recent surveys in the area, identifying the methods and tools used, and discussing challenges, limitations, and ideas for further work.en_US
dc.description.sectionheadersGuidelines and General Considerations
dc.description.seriesinformationVisGap - The Gap between Visualization Research and Visualization Software
dc.identifier.doi10.2312/visgap.20201105
dc.identifier.isbn978-3-03868-125-0
dc.identifier.pages9-16
dc.identifier.urihttps://doi.org/10.2312/visgap.20201105
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/visgap20201105
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectVisualization
dc.subjectVisual analytics
dc.subjectComputing methodologies
dc.subjectDimensionality reduction and manifold learning"
dc.titleSelecting and Sharing Multidimensional Projection Algorithms: A Practical Viewen_US
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VisGap2020 - The Gap between Visualization Research and Visualization Software