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Pré-Publication, Document De Travail Année : 2016

PAC-Bayesian Online Clustering

Résumé

This paper addresses the online clustering problem. When faced with high frequency streams of data, clustering raises theoretical and algorithmic pitfalls. Working under a sparsity assumption, a new online clustering algorithm is introduced. Our procedure relies on the PAC-Bayesian approach, allowing for a dynamic (i.e., time-dependent) estimation of the number of clusters. Its theoretical merits are supported by sparsity regret bounds, and an RJMCMC-flavored implementation called PACO is proposed along with numerical experiments to assess its potential.
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Dates et versions

hal-01264233 , version 1 (28-01-2016)
hal-01264233 , version 2 (07-04-2017)
hal-01264233 , version 3 (08-04-2017)
hal-01264233 , version 4 (25-05-2018)

Identifiants

  • HAL Id : hal-01264233 , version 1

Citer

Le Li, Benjamin Guedj, Sébastien Loustau. PAC-Bayesian Online Clustering. 2016. ⟨hal-01264233v1⟩
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