| dc.contributor.author | Strickert, Marc | en_US |
| dc.contributor.author | Hüllermeier, Eyke | en_US |
| dc.contributor.editor | Mario Hlawitschka and Tino Weinkauf | en_US |
| dc.date.accessioned | 2014-01-26T10:52:45Z | |
| dc.date.available | 2014-01-26T10:52:45Z | |
| dc.date.issued | 2013 | en_US |
| dc.identifier.isbn | 978-3-905673-99-9 | en_US |
| dc.identifier.uri | http://dx.doi.org/10.2312/PE.EuroVisShort.EuroVisShort2013.073-077 | en_US |
| dc.description.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. | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.title | Neighbor Embedding by Soft Kendall Correlation | en_US |
| dc.description.seriesinformation | EuroVis - Short Papers | en_US |
