Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical Study
Machine Learning
2025-02-18 v1 Machine Learning
Abstract
Robust statistics aims to compute quantities to represent data where a fraction of it may be arbitrarily corrupted. The most essential statistic is the mean, and in recent years, there has been a flurry of theoretical advancement for efficiently estimating the mean in high dimensions on corrupted data. While several algorithms have been proposed that achieve near-optimal error, they all rely on large data size requirements as a function of dimension. In this paper, we perform an extensive experimentation over various mean estimation techniques where data size might not meet this requirement due to the high-dimensional setting.
Cite
@article{arxiv.2502.11324,
title = {Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical Study},
author = {Cullen Anderson and Jeff M. Phillips},
journal= {arXiv preprint arXiv:2502.11324},
year = {2025}
}