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• W1392298 abstract "In this paper we consider weighted nonnegative matrix factorizations and we show that the popular algorithms of Lee and Seung can incorporate such a weighting. We then prove that for appropriately chosen weighting matrices, the weighted Euclidean distance function and the weighted generalized Kullback-Leibler divergence function are essentially identical. We finally show that the weighting can be chosen to emphasize parts of the data matrix to be approximated and we can apply this to the low rank fitting of a face image database." @default.
• W1392298 created "2016-06-24" @default.
• W1392298 creator A5036923077 @default.
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• W1392298 date "2007-01-01" @default.
• W1392298 modified "2023-10-01" @default.
• W1392298 title "Weighted Nonnegative Matrix Factorization and Face Feature Extraction" @default.
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• W1392298 hasPublicationYear "2007" @default.
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