arXiv Machine Learning July 7, 2026 By Junjun Pan, Valentin Leplat, Michael Ng, Nicolas Gillis

A Provably-Correct and Robust Convex Model for Smooth Separable NMF

Read the original on arXiv Machine Learning →

arXiv:2511. 07109v2 Announce Type: replace-cross Abstract: Nonnegative matrix factorization (NMF) is a linear dimensionality reduction technique for nonnegative data, with applications such as hyperspectral unmixing and topic modeling.

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benchmarks

arXiv Machine Learning

Aug 27

Maximum-Volume Nonnegative Matrix Factorization

arXiv:2602. 04795v3 Announce Type: replace Abstract: Nonnegative matrix factorization (NMF) is a popular data embedding technique.

By Olivier Vu Thanh, Nicolas Gillis

rag

arXiv Machine Learning

Sep 21

Nonnegative Matrix Factorization in the Component-Wise L1 Norm for Sparse Data

arXiv:2603.29715v2 Announce Type: replace Abstract: Nonnegative matrix factorization (NMF) approximates a nonnegative matrix, X, by the product of two nonnegative factors, WH, where W has r columns a...

By Giovanni Seraghiti, K\'evin Dubrulle, Arnaud Vandaele, Nicolas Gillis

efficiency safety

arXiv Machine Learning

Jul 16

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

arXiv:2607. 13919v1 Announce Type: new Abstract: Nonnegative Matrix Factorization (NMF) is a fundamental tool in unsupervised learning, which approximates a nonnegative matrix by the product of two low-rank nonnegative factors.

By Damien Lesens, J\'er\'emy E. Cohen, Bora U\c{c}ar

benchmarks

arXiv Machine Learning

Sep 1

Separable Nonnegative Matrix Factorization Using Powered Ratio-of-Norms Regularization

arXiv:2608.28799v1 Announce Type: cross Abstract: Separable nonnegative matrix factorization (SNMF) has been widely used for low-rank representation and clustering of nonnegative data, owing to its a...

By Matthew McCarver, Jing Qin

efficiency

arXiv Machine Learning

Jun 9

On solving symmetric multi-type orthogonal non-negative matrix tri-factorization problem

arXiv:2606. 08291v1 Announce Type: new Abstract: We study the symmetric multi-type orthogonal non-negative matrix tri-factorization problem, where several symmetric non-negative matrices are simultaneously approximated by factors of the form $GS_{i}G^{\top}$, with a shared non-negative and orthogonal factor $G$.

By Rok Hribar, Gregor Papa, Janez Povh, Andrej Kastrin

rag benchmarks safety

arXiv Machine Learning

Jun 19

Alternating Direction Method of Multipliers for Nonlinear Matrix Decompositions

arXiv:2512. 17473v3 Announce Type: replace-cross Abstract: We present an algorithm based on the alternating direction method of multipliers (ADMM) for solving nonlinear matrix decompositions (NMD).

By Atharva Awari, Nicolas Gillis, Arnaud Vandaele

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