Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical Study
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910831134900224 |
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| author | Anderson, Cullen Phillips, Jeff M. |
| author_facet | Anderson, Cullen Phillips, Jeff M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11324 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical Study Anderson, Cullen Phillips, Jeff M. Machine Learning 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. |
| title | Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical Study |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.11324 |