Fast Projection‐Based Methods for the Least Squares Nonnegative Matrix Approximation Problem

DOI10.1002/sam.104OpenAlexW4251143728MaRDI QIDQ4969616

Suvrit Sra, Dongmin Kim, Inderjit S. Dhillon

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/sam.104

zbMATH Keywords

least-squaresfactorizationactive setsprojected Newton methodsnonnegative matrix approximation

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

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Uses Software

• NMFLAB
• Multilinear Engine

Cites Work

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• On iterative algorithms for linear least squares problems with bound constraints
• Interior-point gradient method for large-scale totally nonnegative least squares problems
• Algorithms and applications for approximate nonnegative matrix factorization
• On the convergence of the block nonlinear Gauss-Seidel method under convex constraints
• Projected Newton Methods for Optimization Problems with Simple Constraints
• Learning the parts of objects by non-negative matrix factorization

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