Package: glmfsa 1.0.1

glmfsa: Feature Selection with Annealing for Sparse Generalized Linear Models

Fits sparse generalized linear models under an exact cardinality constraint using Feature Selection with Annealing (FSA), the method of Barbu, She, Ding and Gramajo (2017) <doi:10.1109/TPAMI.2016.2544315>. Rather than inducing sparsity through an L1 (lasso-type) penalty, FSA alternates a gradient step on the loss with a step that removes the lowest-magnitude coefficients according to an annealing schedule, so the user specifies the number of variables directly and the surviving coefficients carry no shrinkage bias. The working design matrix physically shrinks as iterations proceed, which makes the cost of a fit fall over time and the method well suited to problems with very many features. Gaussian, binomial, Poisson, multinomial, multi-response Gaussian and Cox proportional hazards families are supported, with robust Lorenz and smoothed support vector machine (SVM) losses available for classification with noisy labels. Dense and sparse design matrices are both handled, and the interface mirrors that of the 'glmnet' package.

Authors:Adrian Barbu [aut, cre, cph], Yiyuan She [aut], Liangjing Ding [ctb], Gary Gramajo [ctb]