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The elastic-net is among the most widely used types of regularizationalgorithms, commonly associated with the problem of supervised generalizedlinear model estimation via penalized maximum likelihood. Its nice propertiesoriginate from a combination of $\ell_1$ and $\ell_2$ norms, which endow thismethod with the ability to select variables taking into account thecorrelations between them. In the last few years, semi-supervised approaches,that use both labeled and unlabeled data, have become an important component inthe statistical research. Despite this interest, however, few researches haveinvestigated semi-supervised elastic-net extensions. This paper introduces anovel solution for semi-supervised learning of sparse features in the contextof generalized linear model estimation: the generalized semi-supervisedelastic-net (s2net), which extends the supervised elastic-net method, with ageneral mathematical formulation that covers, but is not limited to, bothregression and classification problems. We develop a flexible and fastimplementation for s2net in R, and its advantages are illustrated using bothreal and synthetic data sets.

A generalized linear joint trained framework for semi-supervised learning of sparse features