An Alternating Rank-k Nonnegative Least Squares Framework (ARkNLS) for Nonnegative Matrix Factorization

DOI10.1137/20M1352405MaRDI QIDQ5158759

Weya Shi, Srinivas Eswar, Delin Chu, Haesun Park

Publication date: 26 October 2021

Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2007.06118

zbMATH Keywords

block coordinate descent methodnonnegative least squaresnonnegative matrix factorizationrank-\(k\) residue iteration

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Iterative numerical methods for linear systems (65F10) Numerical computation of matrix exponential and similar matrix functions (65F60)

Related Items (2)

Bi-level algorithm for optimizing hyperparameters in penalized nonnegative matrix factorization ⋮ A Learnable Group-Tube Transform Induced Tensor Nuclear Norm and Its Application for Tensor Completion

Uses Software

• SymNMF
• HierNMF2

Cites Work

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