Projected gradient methods for nonnegative matrix factorization
Resource
Neural Computation,19,2756-2779.
Neural Computation 19 (10): 2756-2779
Journal
Neural Computation
Journal Volume
19
Journal Issue
10
Pages
2756-2779
Date Issued
2007
Author(s)
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.
Type
journal article
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