Bayesian Separation of Non-Stationary Mixtures of Dependent Gaus
Shared by Deniz Gencaga, updated on Sep 22, 2010
Summary
- Author(s) :
- Deniz Gencaga, E.E. Kuruoglu, A. Ertuzun
- Abstract
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.
- Publication Name
- Proceedings of the 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
- Publication Location
- Volume 803, pp. 257-265
- Year Published
- 2005
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