An ensemble learning approach to independent component analysis

Independent Component Analysis (ICA) is an important tool for extracting structure from data. ICA is traditionally performed under a maximum likelihood scheme in a latent variable model and in the absence of noise. Although extensively utilised, maximum likelihood estimation has well known drawbacks such as overfitting and sensitivity to local-maxima. In this paper, we propose a Bayesian learning scheme, Variational Bayes or Ensemble Learning, for both latent variables and parameters in the model.