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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Bibliographic Details

Published in:	Journal of global optimization Vol. 68; no. 2; pp. 307 - 328
Main Authors:	Nguyen, Duy Khuong, Ho, Tu Bao
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.06.2017 Springer Springer Nature B.V
Subjects:	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 Access:	Get full text
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