General Projective Maps for Multidimensional Data Projection

dc.contributor.authorLehmann, Dirk J.en_US
dc.contributor.authorTheisel, Holgeren_US
dc.contributor.editorJoaquim Jorge and Ming Linen_US
dc.date.accessioned2016-04-26T08:38:53Z
dc.date.available2016-04-26T08:38:53Z
dc.date.issued2016en_US
dc.description.abstractTo project high-dimensional data to a 2D domain, there are two well-established classes of approaches: RadViz and Star Coordinates. Both are well-explored in terms of accuracy, completeness, distortions, and interaction issues. We present a generalization of both RadViz and Star Coordinates such that it unifies both approaches. We do so by considering the space of all projective projections. This gives additional degrees of freedom, which we use for three things: Firstly, we define a smooth transition between RadViz and Star Coordinates allowing the user to exploit the advantages of both approaches. Secondly, we define a data-dependent magic lens to explore the data. Thirdly, we optimize the new degrees of freedom to minimize distortion. We apply our approach to a number of high-dimensional benchmark datasets.en_US
dc.description.number2en_US
dc.description.sectionheadersVisualizationen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume35en_US
dc.identifier.doi10.1111/cgf.12845en_US
dc.identifier.issn1467-8659en_US
dc.identifier.pages443-453en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12845en_US
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.subjectInformation Visualizationen_US
dc.titleGeneral Projective Maps for Multidimensional Data Projectionen_US
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