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  • Distributionally Robust Semi-Supervised Learning for People-Centric Sensing

    Author(s)
    Chen, Kaixuan
    Yao, Lina
    Zhang, Dalin
    Chang, Xiaojun
    Long, Guodong
    Wang, Sen
    Griffith University Author(s)
    Wang, Sen
    Year published
    2019
    Metadata
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    Abstract
    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 ...
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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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    Conference Title
    33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence
    DOI
    https://doi.org/10.1609/aaai.v33i01.33013321
    Subject
    Artificial intelligence
    Science & Technology
    Computer Science, Artificial Intelligence
    Computer Science, Theory & Methods
    Engineering, Electrical & Electronic
    Publication URI
    http://hdl.handle.net/10072/392540
    Collection
    • Conference outputs

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