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Inégalités d'oracle exactes pour la prédiction d'une matrice en grande dimension

Abstract : We consider the problem of prediction of a high dimensional matrix of size $m \times T$ with noise, meaning that $m T$ is much larger than the sample size $n$. We focus on the trace norm minimization algorithm, but also on other penalizations. It is now well-known that such algorithms can be used for matrix completion, as well as other problems, such as multi-task learning, see \cite{candes-plan2,candes-recht08,candes-plan1,candes-tao1, rohde-tsyb09, MR2417263}. In this work, we propose sharp oracle inequalities in a statistical learning setup.
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Submitted on : Thursday, June 24, 2010 - 8:55:26 AM
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Stéphane Gaiffas, Guillaume Lecué, Alexandre B. Tsybakov. Inégalités d'oracle exactes pour la prédiction d'une matrice en grande dimension. 42èmes Journées de Statistique, 2010, Marseille, France, France. ⟨inria-00494750⟩

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