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.
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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 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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