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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