Risk Bounds For Mode Clustering
Statistics Theory
2015-05-05 v1 Machine Learning
Machine Learning
Statistics Theory
Abstract
Density mode clustering is a nonparametric clustering method. The clusters are the basins of attraction of the modes of a density estimator. We study the risk of mode-based clustering. We show that the clustering risk over the cluster cores --- the regions where the density is high --- is very small even in high dimensions. And under a low noise condition, the overall cluster risk is small even beyond the cores, in high dimensions.
Keywords
Cite
@article{arxiv.1505.00482,
title = {Risk Bounds For Mode Clustering},
author = {Martin Azizyan and Yen-Chi Chen and Aarti Singh and Larry Wasserman},
journal= {arXiv preprint arXiv:1505.00482},
year = {2015}
}