Soft Rank Neighbor Embeddings

Correlation-based multidimensional scaling is proposed for reconstructing pairwise dissimilarity or score relationships in a Euclidean space. Pearson correlation between pairs of objects in source and target space can be directly maximized by gradient methods, while gradient op- timization of Spearman rank correlation prots from a numerically soft formulation introduced in this work. Scale and shift invariance properties of correlation help circumventing typical distance concentration problems.