Permuted NMF: A Simple Algorithm Intended to Minimize the Volume of the Score Matrix
Applications
2013-12-19 v1 Machine Learning
Machine Learning
Abstract
Non-Negative Matrix Factorization, NMF, attempts to find a number of archetypal response profiles, or parts, such that any sample profile in the dataset can be approximated by a close profile among these archetypes or a linear combination of these profiles. The non-negativity constraint is imposed while estimating archetypal profiles, due to the non-negative nature of the observed signal. Apart from non negativity, a volume constraint can be applied on the Score matrix W to enhance the ability of learning parts of NMF. In this report, we describe a very simple algorithm, which in effect achieves volume minimization, although indirectly.
Keywords
Cite
@article{arxiv.1312.5124,
title = {Permuted NMF: A Simple Algorithm Intended to Minimize the Volume of the Score Matrix},
author = {Paul Fogel},
journal= {arXiv preprint arXiv:1312.5124},
year = {2013}
}