Abstract
Distributed data collections are now more and more common due to the emergence of cloud computing, to spatially decentralized businesses, or to the availability of various data sharing web services. Obtain knowledge in such a collection raises then the need of new data mining methods to apply in a decentralized architecture. 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.