Projected gradient methods for non-negative matrix factorization (2007)
| Venue: | Neural Computation |
| Citations: | 76 - 1 self |
BibTeX
@TECHREPORT{Lin07projectedgradient,
author = {Chih-jen Lin},
title = {Projected gradient methods for non-negative matrix factorization},
institution = {Neural Computation},
year = {2007}
}
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
Non-negative matrix factorization (NMF) can be formulated as a minimiza-tion 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 paper, 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. 1
