## Abstract

In the era of datasets of unprecedented sizes, data compression techniques are an attractive approach for speeding up machine learning algorithms. One of the most successful paradigms for achieving good-quality compression is that of coresets: small summaries of data that act as proxies to the original input data. Even though coresets proved to be extremely useful to accelerate unsupervised learning problems, applying them to supervised learning problems may bring unexpected computational bottlenecks.

We show that this is the case for Logistic Regression classification, and hence propose two methods for accelerating the computation of coresets for this problem. When coresets are computed using our methods on three public datasets, computing the coreset and learning from it is, in the worst case, 11 times faster than learning directly from the full input data, and 34 times faster in the best case. Furthermore, our results indicate that our accelerating approaches do not degrade the empirical performance of coresets.

We show that this is the case for Logistic Regression classification, and hence propose two methods for accelerating the computation of coresets for this problem. When coresets are computed using our methods on three public datasets, computing the coreset and learning from it is, in the worst case, 11 times faster than learning directly from the full input data, and 34 times faster in the best case. Furthermore, our results indicate that our accelerating approaches do not degrade the empirical performance of coresets.

Original language | English |
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Title of host publication | DATA 2020 - Proceedings of the 9th International Conference on Data Science, Technology and Applications |

Editors | Slimane Hammoudi, Christoph Quix, Jorge Bernardino |

Pages | 78-89 |

Number of pages | 12 |

Volume | 1 |

ISBN (Electronic) | 9789897584404 |

DOIs | |

Publication status | Published - Jul 2020 |

Event | 9th International Conference on Data Science, Technology and Applications (DATA 2020) - , France Duration: 6 Jul 2020 → 9 Jul 2020 http://www.dataconference.org/ |

### Publication series

Name | DATA 2020 - Proceedings of the 9th International Conference on Data Science, Technology and Applications |
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### Conference

Conference | 9th International Conference on Data Science, Technology and Applications (DATA 2020) |
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Country | France |

Period | 6/07/20 → 9/07/20 |

Internet address |

### Bibliographical note

Winner of the Best Paper Award## Keywords

- data summaries
- logistic regression
- coresets
- Data summaries
- Logistic regression
- Large-data
- Computing time
- Coresets