  Semi-supervised learning aims to learn prediction models from both labeled
and unlabeled samples. There has been extensive research in this area. Among
existing work, generative mixture models with Expectation-Maximization (EM) is
a popular method due to clear statistical properties. However, existing
literature on EM-based semi-supervised learning largely focuses on unstructured
prediction, assuming that samples are independent and identically distributed.
Studies on EM-based semi-supervised approach in structured prediction is
limited. This paper aims to fill the gap through a comparative study between
unstructured and structured methods in EM-based semi-supervised learning.
Specifically, we compare their theoretical properties and find that both
methods can be considered as a generalization of self-training with soft class
assignment of unlabeled samples, but the structured method additionally
considers structural constraint in soft class assignment. We conducted a case
study on real-world flood mapping datasets to compare the two methods. Results
show that structured EM is more robust to class confusion caused by noise and
obstacles in features in the context of the flood mapping application.
