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The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Low-rank matrix completion using alternating minimization

Jain, Prateek, Netrapalli, Praneeth, Sanghavi, Sujay·2013·Proceedings of the forty-fifth annual ACM symposium on Theory of Computing

Abstract

Alternating minimization represents a widely applicable and empirically successful approach for finding low-rank matrices that best fit the given data. For example, for the problem of low-rank matrix completion, this method is believed to be one of the most accurate and efficient, and formed a major component of the winning entry in the Netflix Challenge [17].

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Metadata

Authors
Jain, P., Netrapalli, P., & Sanghavi, S.
Venue
Proceedings of the forty-fifth annual ACM symposium on Theory of Computing
Publisher
ACM
Published
2013-06
Pages
665-674
OpenAlex
W2106005123
Cited by
817
References
2
FWCI
111.58
Type
conference paper
Language
en
Open access
closed

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