Near-optimal Coresets For Least-Squares Regression
Data Structures and Algorithms
2016-11-18 v2 Machine Learning
Abstract
We study (constrained) least-squares regression as well as multiple response least-squares regression and ask the question of whether a subset of the data, a coreset, suffices to compute a good approximate solution to the regression. We give deterministic, low order polynomial-time algorithms to construct such coresets with approximation guarantees, together with lower bounds indicating that there is not much room for improvement upon our results.
Keywords
Cite
@article{arxiv.1202.3505,
title = {Near-optimal Coresets For Least-Squares Regression},
author = {Christos Boutsidis and Petros Drineas and Malik Magdon-Ismail},
journal= {arXiv preprint arXiv:1202.3505},
year = {2016}
}
Comments
To appear in IEEE Transactions on Information Theory