Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)
Marie Szafranski, Yves Grandvalet, Pierre Morizet-mahoudeaux
Hierarchical penalization is a generic framework for incorporating prior informa- tion in the ﬁtting of statistical models, when the explicative variables are organized in a hierarchical structure. The penalizer is a convex functional that performs soft selection at the group level, and shrinks variables within each group. This favors solutions with few leading terms in the ﬁnal combination. The framework, orig- inally derived for taking prior knowledge into account, is shown to be useful in linear regression, when several parameters are used to model the inﬂuence of one feature, or in kernel regression, for learning multiple kernels. Keywords – Optimization: constrained and convex optimization. Supervised learning: regression, kernel methods, sparsity and feature selection.