Streaming constrained binary logistic regression with online standardized data
© 2021 Informa UK Limited, trading as Taylor & Francis Group.
| Veröffentlicht in: | Journal of applied statistics. - 1991. - 49(2022), 6 vom: 14., Seite 1519-1539 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | Journal of applied statistics |
| Schlagworte: | Journal Article Big data data stream logistic regression online learning stochastic approximation stochastic gradient |
| Zusammenfassung: | © 2021 Informa UK Limited, trading as Taylor & Francis Group. Online learning is a method for analyzing very large datasets ('big data') as well as data streams. In this article, we consider the case of constrained binary logistic regression and show the interest of using processes with an online standardization of the data, in particular to avoid numerical explosions or to allow the use of shrinkage methods. We prove the almost sure convergence of such a process and propose using a piecewise constant step-size such that the latter does not decrease too quickly and does not reduce the speed of convergence. We compare twenty-four stochastic approximation processes with raw or online standardized data on five real or simulated data sets. Results show that, unlike processes with raw data, processes with online standardized data can prevent numerical explosions and yield the best results |
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| Beschreibung: | Date Revised 16.07.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
| ISSN: | 0266-4763 |
| DOI: | 10.1080/02664763.2020.1870672 |