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Low-rank matrix completion using alternating minimization
867
Zitationen
3
Autoren
2013
Jahr
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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