Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
- Publisher:
- AAAI Press
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2019, 31 (1), pp. 3321 - 3328
- Issue Date:
- 2019
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Distributionally Robust Semi-Supervised Learning for People-Centric Sensing.pdf | Published version | 1.01 MB |
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Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing. However, humangenerated data inherently suffer from distribution shift in semi-supervised learning due to the diverse biological conditions and behavior patterns of humans. To address this problem, we propose a generic distributionally robust model for semi-supervised learning on distributionally shifted data. Considering both the discrepancy and the consistency between the labeled data and the unlabeled data, we learn the latent features that reduce person-specific discrepancy and preserve task-specific consistency. We evaluate our model in a variety of people-centric recognition tasks on real-world datasets, including intention recognition, activity recognition, muscular movement recognition and gesture recognition. The experiment results demonstrate that the proposed model outperforms the state-of-the-art methods.
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