Non-negative matrix factorization
Non-negative matrix factorization (NMF) is a group of algorithms in multivariate analysis and linear algebra where a matrix is factorized into (usually) two matrices
Usually all three matrices must be non-negative, i.e., all elements must be above or equal to zero. It might be said to be a non-negative version of singular value decomposition.
It was used by a Finish group of researchers in the middle of the 1990s under the name positive matrix factorization. It became more widely known after Lee and Seung's investigations of the properties of the algorithm, and after they published a simple usefull algorithm.
Sources and external links
- Pia Anttila, Pentti Paatero, Unto Tapper, Olli Järvinen. "Source identification of bulk wet deposition in Finland by positive matrix factorization", Atmospheric Environment, 29(14):1705-1718, 1995
- Pentti Paatero, "Least squares formulation of robust non-negative factor analysis", Chemometrics and Intelligent Laboratory Systems, 37(1):23-35, 1997 May.
- Daniel D. Lee and H. Sebastian Seung, "Learning the parts of objects by non-negative matrix factorization", Nature, 401(6755):788-791, 1999 October.
- Daniel D. Lee and H. Sebastian Seung, "Algorithms for Non-negative Matrix Factorization", Advances in Neural Information Processing Systems 13: Proceedings of the 2000 Conference, 556-562, MIT Press, 2001.