Fast Nonnegative Matrix Factorization Algorithms Using Projected Gradient Approaches for Large-Scale Problems
Joint Authors
Cichocki, Andrzej
Zdunek, Rafal
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2008, Issue 2008 (31 Dec. 2008), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2008-07-06
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Recently, a considerable growth of interest in projected gradient (PG) methods has been observed due to their high efficiency in solving large-scale convex minimization problems subject to linear constraints.
Since the minimization problems underlying nonnegative matrix factorization (NMF) of large matrices well matches this class of minimization problems, we investigate and test some recent PG methods in the context of their applicability to NMF.
In particular, the paper focuses on the following modified methods: projected Landweber, Barzilai-Borwein gradient projection, projected sequential subspace optimization (PSESOP), interior-point Newton (IPN), and sequential coordinate-wise.
The proposed and implemented NMF PG algorithms are compared with respect to their performance in terms of signal-to-interference ratio (SIR) and elapsed time, using a simple benchmark of mixed partially dependent nonnegative signals.
American Psychological Association (APA)
Zdunek, Rafal& Cichocki, Andrzej. 2008. Fast Nonnegative Matrix Factorization Algorithms Using Projected Gradient Approaches for Large-Scale Problems. Computational Intelligence and Neuroscience،Vol. 2008, no. 2008, pp.1-13.
https://search.emarefa.net/detail/BIM-509845
Modern Language Association (MLA)
Zdunek, Rafal& Cichocki, Andrzej. Fast Nonnegative Matrix Factorization Algorithms Using Projected Gradient Approaches for Large-Scale Problems. Computational Intelligence and Neuroscience No. 2008 (2008), pp.1-13.
https://search.emarefa.net/detail/BIM-509845
American Medical Association (AMA)
Zdunek, Rafal& Cichocki, Andrzej. Fast Nonnegative Matrix Factorization Algorithms Using Projected Gradient Approaches for Large-Scale Problems. Computational Intelligence and Neuroscience. 2008. Vol. 2008, no. 2008, pp.1-13.
https://search.emarefa.net/detail/BIM-509845
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-509845