Fitting a Graph to One-Dimensional Data
Computational Geometry
2020-10-01 v2 Metric Geometry
Abstract
Given n data points in R^d, an appropriate edge-weighted graph connecting the data points finds application in solving clustering, classification, and regresssion problems. The graph proposed by Daitch, Kelner and Spielman (ICML~2009) can be computed by quadratic programming and hence in polynomial time. While a more efficient algorithm would be preferable, replacing quadratic programming is challenging even for the special case of points in one dimension. We develop a dynamic programming algorithm for this case that runs in O(n^2) time.
Cite
@article{arxiv.1809.02948,
title = {Fitting a Graph to One-Dimensional Data},
author = {Siu-Wing Cheng and Otfried Cheong and Taegyoung Lee and Zhengtong Ren},
journal= {arXiv preprint arXiv:1809.02948},
year = {2020}
}