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Factoring nonnegative matrices with linear programs
Victor Bittorf
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Oznake: computer science;machine learning;unsupervised learning;semi-supervised learning;optimization methods;convex optimization;stochastic optimization
This paper describes a new approach for computing nonnegative matrix factorizations (NMFs) with linear programming. The key idea is a data-driven model for the factorization, in which the most salient features in the data are used to express the remaining features. More precisely, given a data matri ...
Leto: 2012 Vir: videolectures.net
Št. zadetkov: 1
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