Modal-Set Estimation with an Application to Clustering
Abstract
We present a first procedure that can estimate -- with statistical consistency guarantees -- any local-maxima of a density, under benign distributional conditions. The procedure estimates all such local maxima, or $\textit{modal-sets}$, of any bounded shape or dimension, including usual point-modes. In practice, modal-sets can arise as dense low-dimensional structures in noisy data, and more generally serve to better model the rich variety of locally-high-density structures in data. The procedure is then shown to be competitive on clustering applications, and moreover is quite stable to a wide range of settings of its tuning parameter.
Cite
Text
Jiang and Kpotufe. "Modal-Set Estimation with an Application to Clustering." International Conference on Artificial Intelligence and Statistics, 2017.Markdown
[Jiang and Kpotufe. "Modal-Set Estimation with an Application to Clustering." International Conference on Artificial Intelligence and Statistics, 2017.](https://mlanthology.org/aistats/2017/jiang2017aistats-modal/)BibTeX
@inproceedings{jiang2017aistats-modal,
title = {{Modal-Set Estimation with an Application to Clustering}},
author = {Jiang, Heinrich and Kpotufe, Samory},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2017},
pages = {1197-1206},
url = {https://mlanthology.org/aistats/2017/jiang2017aistats-modal/}
}