Author

Bora Uccar

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Preprint Jul 2026

An Efficient Newton Algorithm for Nonnegative Matrix Factorization with the Kullback-Leibler Divergence

This work argues that the KL-NMF method has reached its limits and proposes to use instead the second-order Taylor expansion of the loss, leading to a Newton-type method which provably converges and competes favorably with state-of-the-art algorithms on a large variety of datasets.

Damien Lesens, Jérémy E. Cohen, Bora Uccar · 0 citations