AISTATS 2017poster31 citations
Modal-set estimation with an application to clustering
Heinrich Jiang, Samory Kpotufe
Abstract
We present a 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 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 dense 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.
BibTeX
@InProceedings{pmlr-v54-jiang17c,
title = {{Modal-set estimation with an application to clustering}},
author = {Jiang, Heinrich and Kpotufe, Samory},
booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics},
pages = {1197--1206},
year = {2017},
editor = {Singh, Aarti and Zhu, Jerry},
volume = {54},
series = {Proceedings of Machine Learning Research},
month = {20--22 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v54/jiang17c/jiang17c.pdf},
url = {https://proceedings.mlr.press/v54/jiang17c.html},
abstract = {We present a 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 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 dense 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. }
}