@article{FrieHatz:2008,
  Author =       {M. P. Friedlander and K. Hatz},
  Month =        {March},
  Journal =      {Computational Optimization and Applications},
  Title =        {Computing Nonnegative Tensor Factorizations},
  Year =         2008,
  abstract =     {Nonnegative tensor factorization (NTF) is a
                  technique for computing a parts-based representation
                  of high-dimensional data. NTF excels at exposing
                  latent structures in datasets, and at finding good
                  low-rank approximations to the data. We describe an
                  approach for computing the NTF of a dataset that
                  relies only on iterative linear-algebra techniques
                  and that is comparable in cost to the nonnegative
                  matrix factorization. (The better-known nonnegative
                  matrix factorization is a special case of NTF and is
                  also handled by our implementation.) Some important
                  features of our implementation include mechanisms
                  for encouraging sparse factors and for ensuring that
                  they are equilibrated in norm. The complete Matlab
                  software package is available under the GPL
                  license.},
  volume =       23,
  number =       4,
  pages =        {631-647},
  DOI =          {10.1080/10556780801996244},
}