eprintid: 10064561
rev_number: 16
eprint_status: archive
userid: 608
dir: disk0/10/06/45/61
datestamp: 2018-12-20 11:49:14
lastmod: 2021-09-23 22:31:47
status_changed: 2018-12-20 11:49:14
type: article
metadata_visibility: show
creators_name: Li, L
creators_name: Guedj, B
creators_name: Loustau, S
title: A quasi-Bayesian perspective to online clustering
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Online clustering, quasi-Bayesian learning, minimax regret bounds, reversible jump Markov chain Monte Carlo
note: 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.
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.
date: 2018-01
date_type: published
official_url: https://doi.org/10.1214/18-EJS1479
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1605922
doi: 10.1214/18-EJS1479
lyricists_name: Guedj, Benjamin
lyricists_id: BGUED94
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
publication: Electronic Journal of Statistics
volume: 12
number: 2
pagerange: 3071-3113
issn: 1935-7524
citation:        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 <https://doi.org/10.1214/18-EJS1479>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10064561/1/euclid.ejs.1537430425.pdf