About: Stochastic neighbor embedding originally aims at the reconstruction of given distance 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. The present implementation also allows for handling dissimilarity or score-induced neighborhood topologies and makes use of quasi 2nd order gradient-based (l-)BFGS optimization.

• gradient in xsne_fun.m fixed! (constant factor m was missing)

• symmetry option re-introduced allowing for enabling symmetric and asymmetric versions of SNE and t-SNE

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 (sparse) (asymmetric) distance, dissimilarity, or (negative!) score matrices. Input-output relations are modeled as low-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

• Ver 1.2 Added reconstruction of sparse relationship matrices, Fri Jul 26 16:58:37 CEST 2013