Accurate and fast matrix factorization for low-rank learning.

DOI10.22124/jmm.2021.19892.1723arXiv2104.10785OpenAlexW3203820490MaRDI QIDQ6097244

Faezeh Toutounian, Reshad Hosseini, Unnamed Author, Reza Monsefi

Publication date: 12 June 2023

Full work available at URL: https://arxiv.org/abs/2104.10785

zbMATH Keywords

Krylov subspaceRiemannian optimizationGolub-Kahan bidiagonalizationRitz vectorslow-rank learning

Mathematics Subject Classification ID

Factorization of matrices (15A23) Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

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• Why Are Big Data Matrices Approximately Low Rank?
• Randomized methods for matrix computations
• Stochastic Gradient Descent on Riemannian Manifolds
• Calculating the Singular Values and Pseudo-Inverse of a Matrix

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