Fast Nonnegative Matrix Factorization Algorithms Using Projected Gradient Approaches for Large-Scale Problems
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2008-01-01
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| Series: | Computational Intelligence and Neuroscience |
| Online Access: | http://dx.doi.org/10.1155/2008/939567 |
| Summary: | 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. |
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| ISSN: | 1687-5265 1687-5273 |