Non-negative matrix factorization based on generalized dual divergence
Machine Learning
2019-05-20 v1 Machine Learning
Computation
Abstract
A theoretical framework for non-negative matrix factorization based on generalized dual Kullback-Leibler divergence, which includes members of the exponential family of models, is proposed. A family of algorithms is developed using this framework and its convergence proven using the Expectation-Maximization algorithm. The proposed approach generalizes some existing methods for different noise structures and contrasts with the recently proposed quasi-likelihood approach, thus providing a useful alternative for non-negative matrix factorizations. A measure to evaluate the goodness-of-fit of the resulting factorization is described. This framework can be adapted to include penalty, kernel and discriminant functions as well as tensors.
Cite
@article{arxiv.1905.07034,
title = {Non-negative matrix factorization based on generalized dual divergence},
author = {Karthik Devarajan},
journal= {arXiv preprint arXiv:1905.07034},
year = {2019}
}