DC-NMF: nonnegative matrix factorization based on divide-and-conquer for fast clustering and topic modeling

DOI10.1007/s10898-017-0515-zzbMath1379.90025OpenAlexW2605123798WikidataQ125911372 ScholiaQ125911372MaRDI QIDQ1675578

Haesun Park, Rundong Du, Da Kuang, Barry Drake

Publication date: 2 November 2017

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-017-0515-z

zbMATH Keywords

clusteringnonnegative matrix factorizationdivide and conquerscalable algorithmstopic modelingtext analysisconstrained low rank approximation

Mathematics Subject Classification ID

Factorization of matrices (15A23) Nonconvex programming, global optimization (90C26)

Related Items (2)

A generalized inertial proximal alternating linearized minimization method for nonconvex nonsmooth problems ⋮ An Alternating Rank-k Nonnegative Least Squares Framework (ARkNLS) for Nonnegative Matrix Factorization

Uses Software

• CLUTO
• RCV1
• MALLET
• SymNMF
• SmallK
• HierNMF2

Cites Work

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• Introduction to Information Retrieval
• Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method
• Low-Dimensional Polytope Approximation and Its Applications to Nonnegative Matrix Factorization
• 10.1162/jmlr.2003.3.4-5.993
• Learning the parts of objects by non-negative matrix factorization
• Computing a nonnegative matrix factorization -- provably
• Projected Gradient Methods for Nonnegative Matrix Factorization
• Elements of Information Theory

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