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

About: The package provides a Lagrangian approach to the posterior regularization of given linear mappings. This is important in two cases, (a) when systems are under-determined and (b) when the external model for calculating the mapping is invariant to properties such as scaling. The software may be applied in cases when the external model does not provide its own regularization strategy. In addition, the package allows to rank attributes according to their distortion potential to a given linear mapping.

Version 1.1 (May 23, 2012) memory and time optimizations distderivrel.m now supports assessing the relevance of attribute pairs

Version 1.0 (Nov 9, 2011) * Initial Announcement on mloss.org.

About: Multi-class vector classification based on cost function-driven learning vector quantization , minimizing misclassification.

Initial Announcement on mloss.org.

About: Correlative Matrix Mapping (CMM) provides a supervised linear data mapping into a Euclidean subspace of given dimension. Applications include denoising, visualization, label-specific data preprocessing, and assessment of data attribute pairs relevant for the supervised mapping. Solving auto-association problems yields linear multidimensional scaling, similar to PCA, but usually with more faithful low-dimensional mappings.

Tue Jul 5 14:40:03 CEST 2011 - Bugfixes and cleanups

• single precision data affected pinv(). Now fairer using double precision.
• early stopping did not work properly; now fixed
• Hessian update mode globally controlled via hessmode, 'lbfgs' / 'bfgs'
• distmat.m corrected for rounding problems and extended to distmat(X,Y)
• replaced files: corv.m + corvgrad.m -> corvg.m
• removed unused files: corrmat.m, splitdata.m, traforankapply.m