General Projective Maps for Multidimensional Data Projection
| dc.contributor.author | Lehmann, Dirk J. | en_US |
| dc.contributor.author | Theisel, Holger | en_US |
| dc.contributor.editor | Joaquim Jorge and Ming Lin | en_US |
| dc.date.accessioned | 2016-04-26T08:38:53Z | |
| dc.date.available | 2016-04-26T08:38:53Z | |
| dc.date.issued | 2016 | en_US |
| dc.description.abstract | 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. | en_US |
| dc.description.number | 2 | en_US |
| dc.description.sectionheaders | Visualization | en_US |
| dc.description.seriesinformation | Computer Graphics Forum | en_US |
| dc.description.volume | 35 | en_US |
| dc.identifier.doi | 10.1111/cgf.12845 | en_US |
| dc.identifier.issn | 1467-8659 | en_US |
| dc.identifier.pages | 443-453 | en_US |
| dc.identifier.uri | https://doi.org/10.1111/cgf.12845 | en_US |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | I.3.3 [Computer Graphics] | en_US |
| dc.subject | Picture/Image Generation | en_US |
| dc.subject | Information Visualization | en_US |
| dc.title | General Projective Maps for Multidimensional Data Projection | en_US |