A Kernel-Based Calculation of Information on a Metric Space
Information Theory
2014-05-20 v1 math.IT
Abstract
Kernel density estimation is a technique for approximating probability distributions. Here, it is applied to the calculation of mutual information on a metric space. This is motivated by the problem in neuroscience of calculating the mutual information between stimuli and spiking responses; the space of these responses is a metric space. It is shown that kernel density estimation on a metric space resembles the k-nearest-neighbor approach. This approach is applied to a toy dataset designed to mimic electrophysiological data.
Cite
@article{arxiv.1405.4572,
title = {A Kernel-Based Calculation of Information on a Metric Space},
author = {R. Joshua Tobin and Conor J. Houghton},
journal= {arXiv preprint arXiv:1405.4572},
year = {2014}
}