Structured Regularization for conditional Gaussian Graphical Models
vendredi 4 avril 2014, 9h30 - 10h30
We propose a regularized method for multivariate linear regression when the number of predictors may exceed the sample size. This method is designed to strengthen the estimation and the selection of the relevant input features with three ingredients: it takes advantage of the dependency pattern between the responses by estimating the residual covariance; it performs selection on direct links between predictors and responses; and selection is driven by prior structural information. To this end, we build on a recent reformulation of the multivariate linear regression model to a conditional Gaussian graphical model and propose a new regularization scheme accompanied with an efficient optimization procedure. On top of showing very competitive performance on artificial and real data sets, our method demonstrates capabilities for fine interpretation of its parameters, as illustrated in applications to genetics, genomics and spectroscopy.
– Arxiv preprint: http://arxiv.org/abs/1403.6168
– R package prototype: https://r-forge.r-project.org/R/?group_id=1872