Neighbor Embedding by Soft Kendall Correlation

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Date
2013
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
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@inproceedings{
:10.2312/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
} }
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