Abstract
An expectation-maximization (EM) algorithm for independent component analysis in the
presence of gaussian noise is presented. The estimation of the conditional moments of the source
posterior can be accomplished by maximum a posteriori estimation. The approximate conditional
moments enable the development of an EM algorithm for inferring the most probable sources and
learning the parameter in noisy independent component analysis. Simulation results show that the
proposed method can perform blind source separation of sub-Gaussian mixtures and super-Gaussian
mixtures.
| Original language | English |
|---|---|
| Pages (from-to) | 11-17 |
| Number of pages | 7 |
| Journal | Neural Information Processing - Letters and Reviews |
| Publication status | Published - Jan 2004 |