The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data

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dc.contributor.authorLiu, Shusenen_US
dc.contributor.authorBremer, Peer-Timoen_US
dc.contributor.authorJayaraman, Jayaraman Thiagarajanen_US
dc.contributor.authorWang, Beien_US
dc.contributor.authorSumma, Brianen_US
dc.contributor.authorPascucci, Valerioen_US
dc.contributor.editorKwan-Liu Ma and Giuseppe Santucci and Jarke van Wijken_US
dc.date.accessioned2016-06-09T09:32:31Z
dc.date.available2016-06-09T09:32:31Z
dc.date.issued2016en_US
dc.description.abstractLinear projections are one of the most common approaches to visualize high-dimensional data. Since the space of possible projections is large, existing systems usually select a small set of interesting projections by ranking a large set of candidate projections based on a chosen quality measure. However, while highly ranked projections can be informative, some lower ranked ones could offer important complementary information. Therefore, selection based on ranking may miss projections that are important to provide a global picture of the data. The proposed work fills this gap by presenting the Grassmannian Atlas, a framework that captures the global structures of quality measures in the space of all projections, which enables a systematic exploration of many complementary projections and provides new insights into the properties of existing quality measures.en_US
dc.description.number3en_US
dc.description.sectionheadersHigh-Dimensional Dataen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume35en_US
dc.identifier.doi10.1111/cgf.12876en_US
dc.identifier.issn1467-8659en_US
dc.identifier.pages001-010en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12876en_US
dc.identifier.urihttps://diglib.eg.org:443/handle/10
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.3 [Computer Graphics]en_US
dc.subjectPicture/Image Generationen_US
dc.subjectLine and curve generationen_US
dc.titleThe Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Dataen_US
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35-Issue 3
EuroVis16: Eurographics Conference on Visualization