Inferring Human Activities Using Robust Privileged Probabilistic Learning
This work addresses structure imbalance in classification for human activity recognition, representing an incremental improvement within the LUPI paradigm.
The paper tackled the problem of structure imbalance between training and testing data in classification models by proposing LUPI-HCRF, a supervised probabilistic approach that integrates learning using privileged information into a hidden conditional random field model with robustness to outliers using Student's t-distribution. Experimental results on three datasets showed effectiveness and improved state-of-the-art in the LUPI framework for human activity recognition.
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.