Neighbor Embedding by Soft Kendall Correlation

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dc.contributor.authorStrickert, Marcen_US
dc.contributor.authorHüllermeier, Eykeen_US
dc.contributor.editorMario Hlawitschka and Tino Weinkaufen_US
dc.date.accessioned2014-01-26T10:52:45Z
dc.date.available2014-01-26T10:52:45Z
dc.date.issued2013en_US
dc.description.abstractCorrelation-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.en_US
dc.description.seriesinformationEuroVis - Short Papersen_US
dc.identifier.isbn978-3-905673-99-9en_US
dc.identifier.urihttps://doi.org/10.2312/PE.EuroVisShort.EuroVisShort2013.073-077en_US
dc.publisherThe Eurographics Associationen_US
dc.titleNeighbor Embedding by Soft Kendall Correlationen_US
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