General Projective Maps for Multidimensional Data Projection
To 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 tha...
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Published in | Computer graphics forum Vol. 35; no. 2; pp. 443 - 453 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Oxford
Blackwell Publishing Ltd
01.05.2016
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Subjects | |
Online Access | Get full text |
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Summary: | To 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. |
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Bibliography: | Supporting InformationSupporting InformationSupporting Information istex:A25DDFE6F86A685873B8292E115468577F052835 ark:/67375/WNG-23MZJQR0-D ArticleID:CGF12845 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0167-7055 1467-8659 |
DOI: | 10.1111/cgf.12845 |