Matrix Completion from $O(n)$ Samples in Linear Time


David Gamarnik, Quan Li, Hongyi Zhang ;
Proceedings of the 2017 Conference on Learning Theory, PMLR 65:940-947, 2017.


We consider the problem of reconstructing a rank-$k$ $n \times n$ matrix $M$ from a sampling of its entries. Under a certain incoherence assumption on $M$ and for the case when both the rank and the condition number of $M$ are bounded, it was shown in (Candès and Recht, 2009; Candès and Tao, 2010; Keshavan et al., 2010; Recht, 2011; Jain et al., 2012; Hardt, 2014) that $M$ can be recovered exactly or approximately (depending on some trade-off between accuracy and computational complexity) using $O(n  \text{poly}(\log n))$ samples in super-linear time $O(n^a  \text{poly}(\log n))$ for some constant $a ≥1$. In this paper, we propose a new matrix completion algorithm using a novel sampling scheme based on a union of independent sparse random regular bipartite graphs. We show that under the same conditions w.h.p. our algorithm recovers an $ε$-approximation of $M$ in terms of the Frobenius norm using $O(n \log^2(1/ε))$ samples and in linear time $O(n \log^2(1/ε))$. This provides the best known bounds both on the sample complexity and computational cost for reconstructing (approximately) an unknown low-rank matrix. The novelty of our algorithm is two new steps of thresholding singular values and rescaling singular vectors in the application of the “vanilla” alternating minimization algorithm. The structure of sparse random regular graphs is used heavily for controlling the impact of these regularization steps.

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