arXiv Machine Learning June 24, 2026 By Saghar Bagheri, Gene Cheung, Tim Eadie, Antonio Ortega

Low-rank Updates in Slowly Time-varying Graphs for Spatial-Temporal Signal Interpolation

Read the original on arXiv Machine Learning →

arXiv:2606. 24011v1 Announce Type: cross Abstract: A crucial assumption in graph signal processing (GSP) is the existence of an underlying graph that captures the pairwise similarities between nodes, allowing filters to be designed based on this graph for tasks such as denoising.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 28

Weighted Low-Rank Matrix Approximation: Acceleration and Applications

arXiv:2109. 11057v2 Announce Type: replace-cross Abstract: Weighted low-rank matrix approximation (WLRMA) generalizes classical low-rank approximation and matrix completion by allowing arbitrary elementwise weights.

By Elena Tuzhilina, Trevor Hastie

arXiv Machine Learning

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Exploiting Low-Rank Objective Structure in Discrete Quadratic Optimization

arXiv:2602. 20376v3 Announce Type: replace-cross Abstract: We study the problem of maximizing a complex-valued quadratic form over the $K^{\text{th}}$ roots of unity.

By Ria Stevens, Fangshuo Liao, Barbara Su, Thanasis Hadjidimoulas, Jianqiang Li, Anastasios Kyrillidis

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arXiv Machine Learning

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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

Aug 11

Understanding Alternating Minimization for Matrix Completion

arXiv:1312. 0925v4 Announce Type: replace Abstract: Alternating Minimization is a widely used and empirically successful heuristic for matrix completion and related low-rank optimization problems.

By Moritz Hardt

arXiv Machine Learning

2d ago

High-dimensional nonparametric changepoint detection via low-rank degree-two density projection

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By Guoqing Zhang, Zhaixin Chen

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arXiv Machine Learning

Aug 11

Sparse corruption in low-rank matrix inference: the PCA benchmark

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By Urte Adomaityte, Gabriele Sicuro, Pierpaolo Vivo

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Weighted Low-Rank Matrix Approximation: Acceleration and Applications

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