A Coreset Learning Reality Check

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2301.06163.pdf (993.5 KB)

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https://arxiv.org/abs/2301.06163

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https://doi.org/10.48550/arXiv.2301.06163
 http://hdl.handle.net/11603/26902

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

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https://orcid.org/0000-0002-9900-1972

Date

2023-01-15

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journal articles
preprints
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Abstract

Subsampling algorithms are a natural approach to reduce data size before fitting models on massive datasets. In recent years, several works have proposed methods for subsampling rows from a data matrix while maintaining relevant information for classification. While these works are supported by theory and limited experiments, to date there has not been a comprehensive evaluation of these methods. In our work, we directly compare multiple methods for logistic regression drawn from the coreset and optimal subsampling literature and discover inconsistencies in their effectiveness. In many cases, methods do not outperform simple uniform subsampling.