A non-negative expansion for small Jensen-Shannon Divergences
Machine Learning
2008-10-29 v1
Abstract
In this report, we derive a non-negative series expansion for the Jensen-Shannon divergence (JSD) between two probability distributions. This series expansion is shown to be useful for numerical calculations of the JSD, when the probability distributions are nearly equal, and for which, consequently, small numerical errors dominate evaluation.
Cite
@article{arxiv.0810.5117,
title = {A non-negative expansion for small Jensen-Shannon Divergences},
author = {Anil Raj and Chris H. Wiggins},
journal= {arXiv preprint arXiv:0810.5117},
year = {2008}
}
Comments
4 page technical report, 2 figures