Topological Mixture Estimation
Abstract
We introduce topological mixture estimation, a completely nonparametric and computationally efficient solution to the problem of estimating a one-dimensional mixture with generic unimodal components. We repeatedly perturb the unimodal decomposition of Baryshnikov and Ghrist to produce a topologically and information-theoretically optimal unimodal mixture. We also detail a smoothing process that optimally exploits topological persistence of the unimodal category in a natural way when working directly with sample data. Finally, we illustrate these techniques through examples.
Cite
Text
Huntsman. "Topological Mixture Estimation." International Conference on Machine Learning, 2018.Markdown
[Huntsman. "Topological Mixture Estimation." International Conference on Machine Learning, 2018.](https://mlanthology.org/icml/2018/huntsman2018icml-topological/)BibTeX
@inproceedings{huntsman2018icml-topological,
title = {{Topological Mixture Estimation}},
author = {Huntsman, Steve},
booktitle = {International Conference on Machine Learning},
year = {2018},
pages = {2088-2097},
volume = {80},
url = {https://mlanthology.org/icml/2018/huntsman2018icml-topological/}
}