About: The aim is to embed a given data relationship matrix into a low-dimensional Euclidean space such that the point distances / distance ranks correlate best with the original input relationships. Input relationships may be given as (asymmetric) distances, dissimilarities, or (negative) scores. Input-output relations are modelled as row-conditioned. (Weighted) Pearson and soft Spearman rank correlation, and unweighted soft Kendall correlation are supported correlation measures for input/output object neighborhood relationships.

• Initial release (Ver 1.0): Weighted Pearson and correlation and soft Spearman rank correlation, Tue Dec 4 16:14:51 CET 2012

• Ver 1.1 Added soft Kendall correlation, Fri Mar 8 08:41:09 CET 2013

About: Stochastic neighbor embedding aims at the reconstruction of given distance, dissimilarity, or score neighborhood relations in a low-dimensional Euclidean space. This can be regarded as general approach to multi-dimensional scaling, but the reconstruction is based on the definition of input (and output) neighborhood probability alone. Probability of score exceedance is used for neighborhood probability estimation, which is connected to soft-rank optimization. The present implementation makes use of quasi 2nd order gradient-based (l-)BFGS optimization.

• scoretoprob.m replaced by d2p.m

• protein score data set added

• trank.m computes (mid/max -tied) ranks along columns of matrix

• local P- neighborhood probability estimation added

• experimental soft_rank_SNE added for minimizing KL between probabilities of exceedance in source and embedding space

• symmetry option removed, because this was strange in previous version