halS algorithm 1kullback-leibler divergence 1newton method 1nonnegative matrix factorization 1unsupervised learning 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.LG2026
An Efficient Newton Algorithm for Nonnegative Matrix Factorization with the Kullback-Leibler Divergence
Damien Lesens, Jérémy E. Cohen, Bora Uçar
The paper proposes a Newton-type algorithm for nonnegative matrix factorization using the Kullback-Leibler divergence, employing a second‑order Taylor expansion and a generalized H…
cs.LG2025
dCMF: Learning interpretable evolving patterns from temporal multiway data
Christos Chatzis, Carla Schenker, Jérémy E. Cohen +1
Multiway datasets are commonly analyzed using unsupervised matrix and tensor factorization methods to reveal underlying patterns. Frequently, such datasets include timestamps and c…
cs.LG2025
Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models
Jeremy E. Cohen, Valentin Leplat
Regularized nonnegative low-rank approximations, such as sparse Nonnegative Matrix Factorization or sparse Nonnegative Tucker Decomposition, form an important branch of dimensional…