Optimal spectral norm rates for noisy low-rank matrix completion

Abstract: In this paper we consider the trace regression model where $n$ entries or linear combinations of entries of an unknown $m_1\times m_2$ matrix $A_0$ corrupted by noise are observed. We establish for the nuclear-norm penalized estimator of $A_0$ introduced in \cite{KLT} a general sharp oracle inequality with the spectral norm for arbitrary values of $n,m_1,m_2$ under an incoherence condition on the sampling distribution $\Pi$ of the observed entries. Then, we apply this method to the matrix completion problem. In this case, we prove that it satisfies an optimal oracle inequality for the spectral norm, thus improving upon the only existing result \cite{KLT} concerning the spectral norm, which assumes that the sampling distribution is uniform. Note that our result is valid, in particular, in the high-dimensional setting $m_1m_2\gg n$. Finally we show that the obtained rate is optimal up to logarithmic factors in a minimax sense.