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

About: This package is an implementation of a linear svm solver with a wide class of regularizations on the svm weight vector (l1, l2, mixed norm l1-lq, adaptive lasso). We provide solvers for the classical single task svm problem and for multi-task with joint feature selection or similarity promoting term.

Initial Announcement on mloss.org.