On the Consistency of Metric and Non-Metric K-Medoids
Abstract
We establish the consistency of K-medoids in the context of metric spaces. We start by proving that K-medoids is asymptotically equivalent to K-means restricted to the support of the underlying distribution under general conditions, including a wide selection of loss functions. This asymptotic equivalence, in turn, enables us to apply the work of Parna (1986) on the consistency of K-means. This general approach applies also to non-metric settings where only an ordering of the dissimilarities is available. We consider two types of ordinal information: one where all quadruple comparisons are available; and one where only triple comparisons are available. We provide some numerical experiments to illustrate our theory.
Cite
Text
Jiang and Arias-Castro. "On the Consistency of Metric and Non-Metric K-Medoids." Artificial Intelligence and Statistics, 2021.Markdown
[Jiang and Arias-Castro. "On the Consistency of Metric and Non-Metric K-Medoids." Artificial Intelligence and Statistics, 2021.](https://mlanthology.org/aistats/2021/jiang2021aistats-consistency/)BibTeX
@inproceedings{jiang2021aistats-consistency,
title = {{On the Consistency of Metric and Non-Metric K-Medoids}},
author = {Jiang, He and Arias-Castro, Ery},
booktitle = {Artificial Intelligence and Statistics},
year = {2021},
pages = {2485-2493},
volume = {130},
url = {https://mlanthology.org/aistats/2021/jiang2021aistats-consistency/}
}