Transductive versions of the LASSO and the Dantzig Selector

Pierre Alquier (1, 2) , Mohamed Hebiri (1)

1 LPMA - Laboratoire de Probabilités et Modèles Aléatoires
2 CREST - Centre de Recherche en Économie et Statistique

We consider the linear regression problem, where the number $p$ of covariates is possibly larger than the number $n$ of observations $(x_{i},y_{i})_{i\leq i \leq n}$, under sparsity assumptions. On the one hand, several methods have been successfully proposed to perform this task, for example the LASSO or the Dantzig Selector. On the other hand, consider new values $(x_{i})_{n+1\leq i \leq m}$. If one wants to estimate the corresponding $y_{i}$'s, one should think of a specific estimator devoted to this task, referred by Vapnik as a "transductive" estimator. This estimator may differ from an estimator designed to the more general task "estimate on the whole domain". In this work, we propose a generalized version both of the LASSO and the Dantzig Selector, based on the geometrical remarks about the LASSO in prévious works. The "usual" LASSO and Dantzig Selector, as well as new estimators interpreted as transductive versions of the LASSO, appear as special cases. These estimators are interesting at least from a theoretical point of view: we can give theoretical guarantees for these estimators under hypotheses that are relaxed versions of the hypotheses required in the papers about the "usual" LASSO. These estimators can also be efficiently computed, with results comparable to the ones of the LASSO.

Mots clés: High-dimensional data; LASSO; Mutual coherence; Sparsity; Variable selection; Transduction; Regression estimation

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Pierre Alquier, Mohamed Hebiri. Transductive versions of the LASSO and the Dantzig Selector. 2009. ⟨hal-00390851v2⟩