  Learning and understanding the typical patterns in the daily activities and
routines of people from low-level sensory data is an important problem in many
application domains such as building smart environments, or providing
intelligent assistance. Traditional approaches to this problem typically rely
on supervised learning and generative models such as the hidden Markov models
and its extensions. While activity data can be readily acquired from pervasive
sensors, e.g. in smart environments, providing manual labels to support
supervised training is often extremely expensive. In this paper, we propose a
new approach based on semi-supervised training of partially hidden
discriminative models such as the conditional random field (CRF) and the
maximum entropy Markov model (MEMM). We show that these models allow us to
incorporate both labeled and unlabeled data for learning, and at the same time,
provide us with the flexibility and accuracy of the discriminative framework.
Our experimental results in the video surveillance domain illustrate that these
models can perform better than their generative counterpart, the partially
hidden Markov model, even when a substantial amount of labels are unavailable.
