Accelerated parallel and distributed algorithm using limited internal memory for nonnegative matrix factorization

Nonnegative matrix factorization (NMF) is a powerful technique for dimension reduction, extracting latent factors and learning part-based representation. For large datasets, NMF performance depends on some major issues such as fast algorithms, fully parallel distributed feasibility and limited inter...

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Veröffentlicht in:	Journal of global optimization Jg. 68; H. 2; S. 307 - 328
Hauptverfasser:	Nguyen, Duy Khuong, Ho, Tu Bao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.06.2017 Springer Springer Nature B.V
Schlagworte:	Algorithms Computer Science Convergence Datasets Factorization Mathematics Mathematics and Statistics Operations Research/Decision Theory Optimization Real Functions Search algorithms Cooridinate descent algorithm Parallel and distributed algorithm Non-negative matrix factorization Accelerated anti-lopsided algorithm
ISSN:	0925-5001, 1573-2916
Online-Zugang:	Volltext
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