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Accélération de la factorisation pondérée en matrices non-négatives par projections aléatoires

Abstract : Random projections belong to the major techniques to process big data and have been successfully applied to, e.g., Nonnegative Matrix Factorization (NMF). However, they cannot be applied when weights are associated with the data. In this paper, we thus aim to solve this issue. We propose a novel framework which combines an expectation-maximization strategy with random projections. We experimentally show the proposed framework to significantly speed-up state-of-the-art NMF methods under mild conditions.
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https://hal.archives-ouvertes.fr/hal-02145705
Contributor : Matthieu Puigt <>
Submitted on : Sunday, February 23, 2020 - 10:04:16 PM
Last modification on : Tuesday, January 5, 2021 - 1:04:02 PM
Long-term archiving on: : Sunday, May 24, 2020 - 1:04:51 PM

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Farouk Yahaya, Matthieu Puigt, Gilles Delmaire, Gilles Roussel. Accélération de la factorisation pondérée en matrices non-négatives par projections aléatoires. GRETSI, Aug 2019, Lille, France. ⟨hal-02145705⟩

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