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• W2946453360 abstract "Abstract We extend the approximation-theoretic technique of optimal recovery to the setting of imputing missing values in clustered data, specifically for non-negative matrix factorization (NMF), and develop an implementable algorithm. Under certain geometric conditions, we prove tight upper bounds on NMF relative error, which is the first bound of this type for missing values. We also give probabilistic bounds for the same geometric assumptions. Experiments on image data and biological data show that this theoretically-grounded technique performs as well as or better than other imputation techniques that account for local structure." @default.
• W2946453360 created "2019-05-29" @default.
• W2946453360 creator A5003657237 @default.
• W2946453360 creator A5065423139 @default.
• W2946453360 date "2019-05-24" @default.
• W2946453360 modified "2023-09-26" @default.
• W2946453360 title "Optimal Recovery of Missing Values for Non-negative Matrix Factorization" @default.
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• W2946453360 doi "https://doi.org/10.1101/647560" @default.
• W2946453360 hasPublicationYear "2019" @default.
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