Coresets for Vector Summarization with Applications to Network Graphs

Dan Feldman; Sedat Ozer; Daniela Rus

ICML 2017poster10 citations

Coresets for Vector Summarization with Applications to Network Graphs

Abstract

We provide a deterministic data summarization algorithm that approximates the mean $\bar{p}=\frac{1}{n}\sum_{p\in P} p$ of a set $P$ of $n$ vectors in $\mathbb{R}^d$, by a weighted mean $\tilde{p}$ of a

BibTeX

@InProceedings{pmlr-v70-feldman17a,
  title = 	 {Coresets for Vector Summarization with Applications to Network Graphs},
  author =       {Dan Feldman and Sedat Ozer and Daniela Rus},
  booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning},
  pages = 	 {1117--1125},
  year = 	 {2017},
  editor = 	 {Precup, Doina and Teh, Yee Whye},
  volume = 	 {70},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {06--11 Aug},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v70/feldman17a/feldman17a.pdf},
  url = 	 {https://proceedings.mlr.press/v70/feldman17a.html},
  abstract = 	 {We provide a deterministic data summarization algorithm that approximates the mean $\bar{p}=\frac{1}{n}\sum_{p\in P} p$ of a set $P$ of $n$ vectors in $\mathbb{R}^d$, by a weighted mean $\tilde{p}$ of a