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Faster-than-fast NMF using random projections and Nesterov iterations

Abstract : Random projections have been recently implemented in Nonnegative Matrix Factorization (NMF) to speed-up the NMF computations, with a negligible loss of performance. In this paper, we investigate the effects of such projections when the NMF technique uses the fast Nesterov gradient descent (NeNMF). We experimentally show that structured random projections significantly speed-up NeNMF for very large data matrices.
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https://hal.archives-ouvertes.fr/hal-01859713
Contributor : Matthieu Puigt <>
Submitted on : Thursday, September 13, 2018 - 10:44:27 PM
Last modification on : Tuesday, January 5, 2021 - 1:04:02 PM

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Farouk Yahaya, Matthieu Puigt, Gilles Delmaire, Gilles Roussel. Faster-than-fast NMF using random projections and Nesterov iterations. iTWIST: international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques, Nov 2018, Marseille, France. ⟨hal-01859713⟩

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