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Pré-Publication, Document De Travail Année : 2017

Structured Matrix Estimation and Completion

Résumé

We study the problem of matrix estimation and matrix completion under a general framework. This framework includes several important models as special cases such as the gaussian mixture model, mixed membership model, bi-clustering model and dictionary learning. We consider the optimal convergence rates in a minimax sense for estimation of the signal matrix under the Frobenius norm and under the spectral norm. As a consequence of our general result we obtain minimax optimal rates of convergence for various special models.
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Dates et versions

hal-01557745 , version 1 (06-07-2017)

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Olga Klopp, Yu Lu, Alexandre B. Tsybakov, Harrison H. Zhou. Structured Matrix Estimation and Completion. 2017. ⟨hal-01557745⟩
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