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Fast Nonnegative Matrix Factorization and Completion Using Nesterov Iterations

Abstract : In this paper, we aim to extend Nonnegative Matrix Factorization with Nesterov iterations (Ne-NMF)—well-suited to large-scale problems—to the situation when some entries are missing in the observed matrix. In particular, we investigate the Weighted and Expectation-Maximization strategies which both provide a way to process missing data. We derive their associated extensions named W-NeNMF and EM-W-NeNMF, respectively. The proposed approaches are then tested on simulated nonnegative low-rank matrix completion problems where the EM-W-NeNMF is shown to outperform state-of-the-art methods and the W-NeNMF technique.
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https://hal.archives-ouvertes.fr/hal-01469366
Contributor : Matthieu Puigt <>
Submitted on : Monday, December 3, 2018 - 11:12:08 PM
Last modification on : Tuesday, January 5, 2021 - 1:04:02 PM
Long-term archiving on: : Monday, March 4, 2019 - 3:33:00 PM

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Clément Dorffer, Matthieu Puigt, Gilles Delmaire, Gilles Roussel. Fast Nonnegative Matrix Factorization and Completion Using Nesterov Iterations. 13th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2017), Feb 2017, Grenoble, France. pp.26-35, ⟨10.1007/978-3-319-53547-0_3⟩. ⟨hal-01469366⟩

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