Variational Bayesian Learning of ICA with Missing Data

@article{Chan2003VariationalBL,
  title={Variational Bayesian Learning of ICA with Missing Data},
  author={Kwokleung Chan and Jong-Hwan Lee and Terrence J. Sejnowski},
  journal={Neural Computation},
  year={2003},
  volume={15},
  pages={1991-2011}
}
Missing data are common in real-world data sets and are a problem for many estimation techniques. We have developed a variational Bayesian method to perform independent component analysis (ICA) on high-dimensional data containing missing entries. Missing data are handled naturally in the Bayesian framework by integrating the generative density model. Modeling the distributions of the independent sources with mixture of gaussians allows sources to be estimated with different kurtosis and… CONTINUE READING
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