General Projective Maps for Multidimensional Data Projection.
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| Title: | General Projective Maps for Multidimensional Data Projection. |
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| Authors: | Lehmann, Dirk J.1, Theisel, Holger1 |
| Source: | Computer Graphics Forum. May2016, Vol. 35 Issue 2, p443-453. 11p. 10 Color Photographs, 2 Graphs. |
| Subjects: | Computer graphics research, Computer art, Digital image processing, Data visualization, Information science |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 01677055 |
| DOI: | 10.1111/cgf.12845 |
