5/03/2015 - 14:00 Sylvain Sardy (Université de Genève / Section de mathématiques) Salle 1 - Tour IRMA
Lasso performs model selection and is particularly useful to look for a low dimensional structure with high dimensional data. Yet, the selection of its regularization parameter remains an open problem to which cross validation is only sub-optimal in terms of false discovery rate or true positive rate. We propose a new selection rule for that parameter. We illustrate with two generalized linear models in Cosmology with an Abel and deblurring Poisson inverse problem, and in Cancer research with logistic regression.