This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies
A Decentralized and Robust Approach to Estimating a Probabilistic Mixture Model for Structuring Distributed Data
Lyon France
August 22-August 27
ISBN: 978-0-7695-4513-4
Data sharing services on the web host huge amounts of resources supplied and accessed by millions of users around the world. While the classical approach is a central control over the data set, even if this data set is distributed, there is growing interesting in decentralized solutions, because of good properties (in particularity, privacy and scaling up). In this paper, we explore a machine learning side of this work direction. We propose a novel technique for decentralized estimation of probabilistic mixture models, which are among the most versatile generative models for understanding data sets. More precisely, we demonstrate how to estimate a global mixture model from a set of local models. Our approach accommodates dynamic topology and data sources and is statistically robust, i.e. resilient to the presence of unreliable local models. Such outlier models may arise from local data which are outliers, compared to the global trend, or poor mixture estimation. We report experiments on synthetic data and real geo-location data from Flickr.
Index Terms:
Probabilistic mixture models, Distributed data, Decentralized estimation, Gossip, Robust estimation
Citation:
Ali El Attar, Antoine Pigeau, Marc Gelgon, "A Decentralized and Robust Approach to Estimating a Probabilistic Mixture Model for Structuring Distributed Data," wi-iat, vol. 1, pp.372-379, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies, 2011
Usage of this product signifies your acceptance of the Terms of Use.