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288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

October 2007, Vol. 19, No. 10, Pages 2756-2779
(doi: 10.1162/neco.2007.19.10.2756)
© 2007 Massachusetts Institute of Technology
Projected Gradient Methods for Nonnegative Matrix Factorization
Article PDF (149.78 KB)
Abstract

Nonnegative matrix factorization (NMF) can be formulated as a minimization problem with bound constraints. Although bound-constrained optimization has been studied extensively in both theory and practice, so far no study has formally applied its techniques to NMF. In this letter, we propose two projected gradient methods for NMF, both of which exhibit strong optimization properties. We discuss efficient implementations and demonstrate that one of the proposed methods converges faster than the popular multiplicative update approach. A simple Matlab code is also provided.