Non-negative Matrix Factorization (NMF)

Chih-Jen Lin


Introduction

This tool solves NMF by alternative non-negative least squares using projected gradients. It converges faster than the popular multiplicative update approach. Details and comparisons are in the following paper:

C.-J. Lin. Projected gradient methods for non-negative matrix factorization. Neural Computation, 19(2007), 2756-2779.

If you find this tool useful, please cite the above work.


Download MATLAB Scripts

Please download nmf.m. The usage is

[W,H] = nmf(V,Winit,Hinit,tol,timelimit,maxiter);

Output/input parameters are

Please read the COPYRIGHT notice before using it.

This code works under Octave as well.

Frequently asked questions


Implementations in other programming languages


Sources for comparing different methods for solving NMF

We compared our approach with the popular multiplicative update method in the above paper. All sources for experiments are in this zip file. The current version is 1.01. You can thus easily regenerate our results. After unzipping it, a directory including all files will be created. Then follow instructions in the README file.

Note that some sources here cannot be used under Octave.


Please send comments and suggestions to Chih-Jen Lin.