Langville, A., Meyer, C., Albright, R., Cox, J. and Duling, D. (2006) Algorithms, Initializations, and Convergence for the Nonnegative Matrix Factorization. NCSU. Eprint Arxiv. http://langvillea.people.cofc.edu/NMFInitAlgConv.pdf

has been cited by the following article:

• TITLE: CUR Based Initialization Strategy for Non-Negative Matrix Factorization in Application to Hyperspectral Unmixing

  AUTHORS: Li Sun, Gengxin Zhao, Xinpeng Du

  KEYWORDS: Nonnegative Matrix Factorization, Hyperspectral Image, Hyperspectral Unmixing, Initialization Method

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.4 No.4, April 13, 2016

  ABSTRACT: Hyperspectral unmixing is a powerful tool for the remote sensing image mining. Nonnegative matrix factorization (NMF) has been adopted to deal with this issue, while the precision of unmixing is closely related with the local minimizers of NMF. We present two novel initialization strategies that is based on CUR decomposition, which is physically meaningful. In the experimental test, NMF with the new initialization method is used to unmix the urban scene which was captured by airborne visible/infrared imaging spectrometer (AVIRIS) in 1997, numerical results show that the initialization methods work well.