About: FABIA is a biclustering algorithm that clusters rows and columns of a matrix simultaneously. Consequently, members of a row cluster are similar to each other on a subset of columns and, analogously, members of a column cluster are similar to each other on a subset of rows. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. Applications include detection of transcriptional modules in gene expression data and identification of haplotypes/>identity by descent< consisting of rare variants obtained by next generation sequencing.

CHANGES IN VERSION 2.4.0

o spfabia bugfixes

CHANGES IN VERSION 2.3.1

NEW FEATURES

o Getters and setters for class Factorization

2.0.0:

• spfabia: fabia for a sparse data matrix (in sparse matrix format) and sparse vector/matrix computations in the code to speed up computations. spfabia applications: (a) detecting >identity by descent< in next generation sequencing data with rare variants, (b) detecting >shared haplotypes< in disease studies based on next generation sequencing data with rare variants;
• fabia for non-negative factorization (parameter: non_negative);
• changed to C and removed dependencies to Rcpp;
• improved update for lambda (alpha should be smaller, e.g. 0.03);
• introduced maximal number of row elements (lL);
• introduced cycle bL when upper bounds nL or lL are effective;
• reduced computational complexity;
• bug fixes: (a) update formula for lambda: tighter approximation, (b) corrected inverse of the conditional covariance matrix of z;

1.4.0:

• New option nL: maximal number of biclusters per row element;
• Sort biclusters according to information content;
• Improved and extended preprocessing;
• Update to R2.13

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.

About: This package is a set of Matlab scripts that implements the algorithms described in the submitted paper: "Lp-Lq Sparse Linear and Sparse Multiple Kernel MultiTask Learning".

Initial Announcement on mloss.org.

About: The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference.

contributed by George Papandreou:

• preconditioning support in the inf/linsolve_lcg.m CG routine.

• @matConv2 and @matFD2 support different boundary conditions in deblurring

• various mat/@*/diagFAtAFt.m support circulant preconditioning

• bugfixes in nonnegativity option in pls/plsLBFGS.m and pen/penVBNorm.m when used together with EP

• inf/diag_inv_sample.m, a Monte Carlo estimator

gfortran support to pls/lbfgsb/Makefile (thanks to Ernst Kloppenburg)

slight modification to mat/@matFFTN/mvm.m to make it more consistent

simple gradient solver using Barzilai-Borwein step size pls/plsBB.m

About: DAL is an efficient and flexibible MATLAB toolbox for sparse learning/reconstruction based on the augmented Lagrangian method.

• 35% faster group lasso.
• Sparse connectivity inference example added (s_test_hsgl.m).
• Non-negative lasso (thanks to Shigeyuki Oba).
• Uses Mark Tygert's pca.m for SVD (PROPACK is not required anymore).

About: Matlab implementation of variational gaussian approximate inference for Bayesian Generalized Linear Models.

Minor bug fix.

About: The package estimates the matrix of partial correlations based on different regularized regression methods: lasso, adaptive lasso, PLS, and Ridge Regression.

Initial Announcement on mloss.org.