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We consider the problem of reconstructing a low rank matrix from a subset of its entries and analyze two variants of the so-called Alternating Minimization algorithm, which has been proposed in the past.
Rank minimization via online learning
R. Meka, P. Jain, C. Caramanis, and I. S. Dhillon · 2008
Earlier work this paper cites.
Exact matrix completion via convex optimization
Emmanuel J Candès and Benjamin Recht · 2009
Earlier work this paper cites.
The power of convex relaxation: near optimal matrix completion
E. Candes and T. Tao · 2009
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Matrix completion from a few entries
Raghunandan H Keshavan, Sewoong Oh, and Andrea Montanari · 2009
Cited alongside, same era.
Matrix completion from noisy entries
Raghunandan Keshavan, Andrea Montanari, and Sewoong Oh · 2009
Cited alongside, same era.
Matrix factorization techniques for recommender systems
Y. Koren, R. M. Bell, and C. Volinsky · 2009
Cited alongside, same era.
The BellKor solution to the Netflix grand prize
Y. Koren · 2009
Later among the works it cites.
Low-rank matrix completion using alternating minimization
Prateek Jain, Praneeth Netrapalli, and Sujay Sanghavi · 2013
Later among the works it cites.
Phase transitions and sample complexity in bayes-optimal matrix factorization
Y. Kabashima, F. Krzakala, M. Mézard, A. Sakata, and L. Zdeborová · 2014
Later among the works it cites.
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