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arXiv · 2502.11324

Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical Study

Cullen Anderson·Jeff M. Phillips
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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.

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Cullen Anderson, Jeff M. Phillips. 2025-02-17. Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical Study. https://arxiv.org/abs/2502.11324

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