Consistent K-Median: Simpler, Better and Robust
Abstract
In this paper we introduce and study the online consistent k-clustering with outliers problem, generalizing the non-outlier version of the problem studied in Lattanzi-Vassilvitskii [18]. We show that a simple local-search based on-line algorithm can give a bicriteria constant approximation for the problem with O(k^2 log^2(nD)) swaps of medians (recourse) in total, where D is the diameter of the metric. When restricted to the problem without outliers, our algorithm is simpler, deterministic and gives better approximation ratio and recourse, compared to that of Lattanzi-Vassilvitskii [18].
Cite
Text
Guo et al. " Consistent K-Median: Simpler, Better and Robust ." Artificial Intelligence and Statistics, 2021.Markdown
[Guo et al. " Consistent K-Median: Simpler, Better and Robust ." Artificial Intelligence and Statistics, 2021.](https://mlanthology.org/aistats/2021/guo2021aistats-consistent/)BibTeX
@inproceedings{guo2021aistats-consistent,
title = {{ Consistent K-Median: Simpler, Better and Robust }},
author = {Guo, Xiangyu and Kulkarni, Janardhan and Li, Shi and Xian, Jiayi},
booktitle = {Artificial Intelligence and Statistics},
year = {2021},
pages = {1135-1143},
volume = {130},
url = {https://mlanthology.org/aistats/2021/guo2021aistats-consistent/}
}