General Projective Maps for Multidimensional Data Projection.
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| Title: | General Projective Maps for Multidimensional Data Projection. |
|---|---|
| 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] |
| Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 115774594 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: General Projective Maps for Multidimensional Data Projection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lehmann%2C+Dirk+J%2E%22">Lehmann, Dirk J.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Theisel%2C+Holger%22">Theisel, Holger</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Graphics+Forum%22">Computer Graphics Forum</searchLink>. May2016, Vol. 35 Issue 2, p443-453. 11p. 10 Color Photographs, 2 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+graphics+research%22">Computer graphics research</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+art%22">Computer art</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+image+processing%22">Digital image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Data+visualization%22">Data visualization</searchLink><br /><searchLink fieldCode="DE" term="%22Information+science%22">Information science</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/cgf.12845 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 443 Subjects: – SubjectFull: Computer graphics research Type: general – SubjectFull: Computer art Type: general – SubjectFull: Digital image processing Type: general – SubjectFull: Data visualization Type: general – SubjectFull: Information science Type: general Titles: – TitleFull: General Projective Maps for Multidimensional Data Projection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lehmann, Dirk J. – PersonEntity: Name: NameFull: Theisel, Holger IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 01677055 Numbering: – Type: volume Value: 35 – Type: issue Value: 2 Titles: – TitleFull: Computer Graphics Forum Type: main |
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