Fast Nonnegative Matrix Factorization and Completion Using Nesterov Iterations

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10169)


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.


Low-rank matrix completion Nonnegative matrix factorization Nesterov iterations Gradient descent 



This work was funded by the “OSCAR” project within the Région Hauts-de-France “Chercheurs Citoyens” Program.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Univ. Littoral Côte d’Opale, LISIC – EA 4491CalaisFrance

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