Hugging Face Trending Papers June 2, 2026

Graph Regularized Non-negative Reduced Biquaternion Matrix Factorization for Color Image Recognition

Read the original on Hugging Face Trending Papers →

Non-negative reduced biquaternion matrix factorization (NRBMF) uses the product of reduced biquaternion (RB) matrices to incorporate the non-negativity constraints of color image pixels into the factorization process. However, NRBMF mainly focuses on reconstruction accuracy and does not exploit the local geometric structure of image data, which may limit the discriminative ability of the learned low-dimensional features.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

computer-vision

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 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

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 AI

Jun 30

Beyond Spectral Decomposition: Bayesian Contrastive Learning and its Non-negative Formulation via Factor Analysis

arXiv:2407. 21740v3 Announce Type: replace-cross Abstract: Factor analysis, often regarded as a Bayesian variant of matrix factorization, offers superior capabilities in capturing uncertainty, modeling complex dependencies, and ensuring robustness.

By Zhibin Duan, Tiansheng Wen, Yifei Wang, Chen Zhu, Bo Chen, Mingyuan Zhou

safety

arXiv Machine Learning

5d ago

Difference-of-Convex Regularization for Graph Learning by Differentiable Programming

arXiv:2608. 12757v1 Announce Type: cross Abstract: Laplacian-regularized minimization is fundamental in signal processing and machine learning, but is limited by the dense and ill-conditioned nature of the graph Laplacian pseudoinverse.

By Liping Tao, Chee Wei Tan

arXiv Machine Learning

Jul 22

Generalized Least Squares Kernelized Tensor Factorization

arXiv:2412. 07041v4 Announce Type: replace-cross Abstract: Recovering incomplete multidimensional tensor-structured data is a fundamental task in many real-world applications.

By Mengying Lei, Lijun Sun

efficiency

Related stories

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

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

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

Beyond Spectral Decomposition: Bayesian Contrastive Learning and its Non-negative Formulation via Factor Analysis

Difference-of-Convex Regularization for Graph Learning by Differentiable Programming

Generalized Least Squares Kernelized Tensor Factorization