@inproceedings{4ca4a96eb386467bb0b91204ae74786b,
title = "An Online Minorization-Maximization Algorithm",
abstract = "Modern statistical and machine learning settings often involve high data volume and data streaming, which require the development of online estimation algorithms. The online Expectation–Maximization (EM) algorithm extends the popular EM algorithm to this setting, via a stochastic approximation approach.We show that an online version of the Minorization–Maximization (MM) algorithm, which includes the online EM algorithm as a special case, can also be constructed in a similar manner. We demonstrate our approach via an application to the logistic regression problem and compare it to existing methods.",
keywords = "Expectation–maximization, Minorization-maximization, Online algorithms, Parameter estimation, Stochastic approximation",
author = "Nguyen, \{Hien Duy\} and Florence Forbes and Gersende Fort and Olivier Capp{\'e}",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s).; 17th Conference of the International Federation of Classification Societies, IFCS 2022 ; Conference date: 19-07-2022 Through 23-07-2022",
year = "2023",
month = jan,
day = "1",
doi = "10.1007/978-3-031-09034-9\_29",
language = "English",
isbn = "9783031090332",
series = "Studies in Classification, Data Analysis, and Knowledge Organization",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "263--271",
editor = "Paula Brito and Paula Brito and Dias, \{Jos{\'e} G.\} and Berthold Lausen and Angela Montanari and Rebecca Nugent",
booktitle = "Classification and Data Science in the Digital Age - 17th Conference of the International Federation of Classification Societies, IFCS 2022, Proceedings",
}