PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Gencaga D., Kuruoglu E. E., Ertuzun A. Bayesian separation of non-stationary mixtures of dependent gaussian sources. In: International Workshop on Bayesian Inference and Maximum Entropy (San Josť CA USA, August 7-12, 2005). Proceedings, vol. 803 pp. 257 - 265. 2005.
 
 
Abstract
(English)
In this work, we propose a novel approach to perform Dependent Component Analysis (DCA). DCA can be thought as the separation of latent, dependent sources from their observed mixtures which is a more realistic model than Independent Component Analysis (ICA) where the sources are assumed to be independent. In general, the sources can be spatiotemporally dependent and the mixing system may be non-stationary. Here, we propose a DCA algorithm, that combines concepts of particle filters and Markov Chain Monte Carlo (MCMC) methods in order to separate non-stationary mixtures of spatially dependent Gaussian sources.
URL: http://ic.arc.nasa.gov/workshops/maxent2005/
Subject Source separation
Dependent component analysis
Bayesian source separation
Markov Chain Monte Carlo
Particle filtering
Gaussian processes
G.3 Probability and statistics. Probabilistic algorithms
G.3 Probability and statistics. Stochastic processes


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