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A quasi-Bayesian perspective to online clustering

Li, L; Guedj, B; Loustau, S; (2018) A quasi-Bayesian perspective to online clustering. Electronic Journal of Statistics , 12 (2) pp. 3071-3113. 10.1214/18-EJS1479. Green open access

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Abstract

When faced with high frequency streams of data, clustering raises theoretical and algorithmic pitfalls. We introduce a new and adaptive online clustering algorithm relying on a quasi-Bayesian approach, with a dynamic (i.e., time-dependent) estimation of the (unknown and changing) number of clusters. We prove that our approach is supported by minimax regret bounds. We also provide an RJMCMC-flavored implementation (called PACBO, see https://cran.r-project.org/web/packages/PACBO/index.html) for which we give a convergence guarantee. Finally, numerical experiments illustrate the potential of our procedure.

Type: Article
Title: A quasi-Bayesian perspective to online clustering
Open access status: An open access version is available from UCL Discovery
DOI: 10.1214/18-EJS1479
Publisher version: https://doi.org/10.1214/18-EJS1479
Language: English
Additional information: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: Online clustering, quasi-Bayesian learning, minimax regret bounds, reversible jump Markov chain Monte Carlo
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10064561
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