On-line gossip-based distributed expectation maximization algorithm - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès SSP '12 : IEEE Statistical Signal Processing Workshop Année : 2012

On-line gossip-based distributed expectation maximization algorithm

Résumé

In this paper, we introduce a novel on-line Distributed Expectation-Maximization (DEM) algorithm for latent data models including Gaussian Mixtures as a special case. We consider a network of agents whose mission is to estimate a parameter from the time series locally observed by the agents. Our estimator works online and asynchronously: it starts processing data as they arrive with no need of a reference clock, common to all the agents. Agents update some local summary statistics using recent data (E-step), then share these statistics with theirs neighbors in order to eventually reach a consensus (gossip step), and finally use them to generate individual estimates of the unknown parameter (M-step). Our algorithm is shown to converge under mild conditions on the gossip protocol, freeing the network from feedback communications; hence making this DEM algorithm particularly well suited to Wireless Sensor Networks (WSN).
Fichier non déposé

Dates et versions

hal-00755575 , version 1 (21-11-2012)

Identifiants

Citer

Gemma Morral, Pascal Bianchi, Jérémie Jakubowicz. On-line gossip-based distributed expectation maximization algorithm. SSP '12 : IEEE Statistical Signal Processing Workshop, Aug 2012, Ann Arbor, United States. pp.305-308, ⟨10.1109/SSP.2012.6319689⟩. ⟨hal-00755575⟩
77 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More