Adaptive computation of the Symmetric Nonnegative Matrix Factorization (NMF)
Numerical Analysis
2019-03-05 v1
Abstract
Nonnegative Matrix Factorization (NMF), first proposed in 1994 for data analysis, has received successively much attention in a great variety of contexts such as data mining, text clustering, computer vision, bioinformatics, etc. In this paper the case of a symmetric matrix is considered and the symmetric nonnegative matrix factorization (SymNMF) is obtained by using a penalized nonsymmetric minimization problem. Instead of letting the penalizing parameter increase according to an a priori fixed rule, as suggested in literature, we propose a heuristic approach based on an adaptive technique. Extensive experimentation shows that the proposed algorithm is effective.
Cite
@article{arxiv.1903.01321,
title = {Adaptive computation of the Symmetric Nonnegative Matrix Factorization (NMF)},
author = {Paola Favati and Grazia Lotti and Ornella Menchi and Francesco Romani},
journal= {arXiv preprint arXiv:1903.01321},
year = {2019}
}
Comments
Submitted to SeMA Journal