Edited by author 10-30-2001 01:56 PM
In principle one could penalize the likelihood in the EM algorithm by whatever seems to be reasonable considering the target application, but my understanding is that it is not anymore guaranteed that the EM algorithm will converge. This will of course depend on what exact kind of penalization one uses and how strong the contribution of the penalty term has. An example I have in mind is one where the likelihood is penalized by a Markov random field type term that forces EM to preferentially group neighboring pixels together. This type of penalization leads to problems with convegence if the penalty term is too large (i.e. the Gibbs field strength is too large).