Strickert, MarcHüllermeier, EykeMario Hlawitschka and Tino Weinkauf2014-01-262014-01-262013978-3-905673-99-9https://doi.org/10.2312/PE.EuroVisShort.EuroVisShort2013.073-077Correlation-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.