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    Neighbor Embedding by Soft Kendall Correlation

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    073-077.pdf (441.1Kb)
    Date
    2013
    Author
    Strickert, Marc
    Hüllermeier, Eyke
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    Abstract
    Correlation-based embedding of complex data relationships in a Euclidean space is studied. The proposed soft formulation of Kendall correlation allows for gradient-based optimization of scatter point neighborhood relationships for reconstructing original data neighbors. The approach is able to handle asymmetric data relations provided in the form of a general scoring matrix. Scale and shift invariance properties of correlation help circumventing typical embedding distortion artefacts in dimension reduction and data embedding scenarios.
    BibTeX
    @inproceedings {PE.EuroVisShort.EuroVisShort2013.073-077,
    booktitle = {EuroVis - Short Papers},
    editor = {Mario Hlawitschka and Tino Weinkauf},
    title = {{Neighbor Embedding by Soft Kendall Correlation}},
    author = {Strickert, Marc and Hüllermeier, Eyke},
    year = {2013},
    publisher = {The Eurographics Association},
    ISBN = {978-3-905673-99-9},
    DOI = {10.2312/PE.EuroVisShort.EuroVisShort2013.073-077}
    }
    URI
    http://dx.doi.org/10.2312/PE.EuroVisShort.EuroVisShort2013.073-077
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    System hosted at  Graz University of Technology.
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