Hugging Face Trending Papers

Maximum-distance nonnegative matrix factorization for unmixing highly mixed grain-size distribution data: A generalization of AnalySize

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

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

Jul 23

Non--negative matrix factorization using the \textit{R} package \textsf{nnmf}

arXiv:2607. 20084v1 Announce Type: cross Abstract: Non--negative matrix factorization (NMF) has become an established dimensionality reduction technique for extracting latent structures from non--negative data and has found widespread applications in fields such as bioinformatics, text mining, image analysis, and recommender systems.

By Volkan Sevin\c{c}, Nikolas Kontemeniotis, Theodoros Perdikis, Michail Tsagris

arXiv Statistics ML

Aug 25

Subzero matrix completion for sparse data analysis: large-scale learning of latent low-rank structure

arXiv:2608.21607v1 Announce Type: cross Abstract: We investigate when a sparse nonnegative matrix can be recovered from a real-valued matrix of much lower rank by zeroing out its negative elements. T...

By Lawrence K. Saul, Ningyuan Huang, Dennis Bollweg, Jeff Soules, Diana C. Halikias

efficiency

arXiv Machine Learning

Jul 7

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

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.

By Junjun Pan, Valentin Leplat, Michael Ng, Nicolas Gillis

benchmarks

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

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 10

Time series forecasting from partial observations via Non-negative Matrix Factorization

arXiv:2102. 05314v2 Announce Type: replace Abstract: In modern time series problems, one aims at forecasting multiple time series with possible missing and noisy values.

By Yohann de Castro (ICJ, PSPM, CERMICS UMR 9032, ECL, IUF), Luca Mencarelli (CERMICS UMR 9032)

llms benchmarks

arXiv Machine Learning

2d ago

Nonnegative matrix factorizations and related compositional models: Equivalence, identifiability, and an application on the grain-size analysis of sediments

arXiv:2512.22282v2 Announce Type: replace-cross Abstract: Across fields such as machine learning, social science, and geology, considerable attention has been given to models that factorize a nonnega...

By Qianqian Qi, Peter G. M. van der Heijden, Maarten A. Prins

arXiv Machine Learning

Jun 4

Low-rank Distributional Matrix Completion

arXiv:2606. 04176v1 Announce Type: new Abstract: We study a distributional generalization of the matrix completion problem in which each entry of the target matrix is a probability distribution rather than a scalar.

By Jiayi Wang, Raymond K. W. Wong

rag

arXiv Machine Learning

Jun 3

A Nonmonotone Gradient-Based Algorithm for Symmetric Nonnegative Matrix Factorization and Graph Clustering

arXiv:2606. 02887v1 Announce Type: new Abstract: Symmetric nonnegative matrix factorization (Symmetric NMF) approximates a matrix as $WW^T$ with nonnegative rectangular factor $W$.

By Ryan Swart, Johannes Brust

benchmarks

arXiv Machine Learning

Jun 19

Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings

arXiv:2104. 08928v4 Announce Type: replace-cross Abstract: Unstructured text provides decision-makers with a rich data source in many domains, ranging from product reviews in retail to nursing notes in healthcare.

By Kan Xu, Xuanyi Zhao, Hamsa Bastani, Osbert Bastani

rag nlp fine-tuning benchmarks

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