  Classification models may often suffer from "structure imbalance" between
training and testing data that may occur due to the deficient data collection
process. This imbalance can be represented by the learning using privileged
information (LUPI) paradigm. In this paper, we present a supervised
probabilistic classification approach that integrates LUPI into a hidden
conditional random field (HCRF) model. The proposed model is called LUPI-HCRF
and is able to cope with additional information that is only available during
training. Moreover, the proposed method employes Student's t-distribution to
provide robustness to outliers by modeling the conditional distribution of the
privileged information. Experimental results in three publicly available
datasets demonstrate the effectiveness of the proposed approach and improve the
state-of-the-art in the LUPI framework for recognizing human activities.
