Bayesian Separation of Non-Stationary Mixtures of Dependent Gaus
Description
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 spatio-temporally 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.
Resources
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Description |
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Bayesian Separation of Non-Stationary Mixtures of Dependent Gaussian Sources |
https://c3.nasa.gov/dashlink/static/media/publication/MAXENT_2005.pdf |