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.
Lin REF employed a projected gradient bound-constrained optimization method which has better convergence properties than the multiplicative update.
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Neural Computation
Mathematics Computer Science Medicine