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    General Projective Maps for Multidimensional Data Projection

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    Date
    2016
    Author
    Lehmann, Dirk J.
    Theisel, Holger
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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.
    BibTeX
    @article {10.1111:cgf.12845,
    journal = {Computer Graphics Forum},
    title = {{General Projective Maps for Multidimensional Data Projection}},
    author = {Lehmann, Dirk J. and Theisel, Holger},
    year = {2016},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.12845}
    }
    URI
    http://dx.doi.org/10.1111/cgf.12845
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    • 35-Issue 2
    • EG 2016 - Full Papers - CGF 35-Issue 2

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    System hosted at  Graz University of Technology.
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