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/}
}