M. P. Friedlander and K. Hatz
Optimization Methods and Software, 23(4):631-647, August 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.
BibTeX
@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},
}