Combining a naive Bayes classifier with the EM algorithm is one of the promising minimally supervised approaches because its computational cost is low (linear to the size of unlabeled data), and it does not require the features to be split into two independent sets unlike cotraining.
the use of unlabeled data via the basic EM algorithm does not always improve classification performance.