The relationship among three correlated variables could be very sophisticated, as a result, we may not be able to find their hidden causality and model their relationship explicitly. However, we still can make our best guess for possible mappings among these variables, based on the observed relationship. One of the complicated relationships among three correlated variables could be a two-layer hierarchical many-to-many mapping. In this paper, we proposed a Hierarchical Mixture Density Network (HMDN) to model the two-layer hierarchical many-to-many mapping. We apply HMDN on an indoor positioning problem and show its benefit.
@article{arxiv.1910.13523,
title = {A Hierarchical Mixture Density Network},
author = {Fan Yang and Jaymar Soriano and Takatomi Kubo and Kazushi Ikeda},
journal= {arXiv preprint arXiv:1910.13523},
year = {2019}
}