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  • Learning from Less for Better: Semi-Supervised Activity Recognition via Shared Structure Discovery

    Author(s)
    Yao, Lina
    Nie, Feiping
    Sheng, Quan Z
    Gu, Tao
    Li, Xue
    Wang, Sen
    Griffith University Author(s)
    Wang, Sen
    Year published
    2016
    Metadata
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    Abstract
    Despite the active research into, and the development of, human activity recognition over the decades, existing techniques still have several limitations, in particular, poor performance due to insufficient ground-truth data and little support of intra-class variability of activities (i.e., the same activity may be performed in different ways by different individuals, or even by the same individuals with different time frames). Aiming to tackle these two issues, in this paper, we present a robust activity recognition approach by extracting the intrinsic shared structures from activities to handle intra-class variability, and ...
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    Despite the active research into, and the development of, human activity recognition over the decades, existing techniques still have several limitations, in particular, poor performance due to insufficient ground-truth data and little support of intra-class variability of activities (i.e., the same activity may be performed in different ways by different individuals, or even by the same individuals with different time frames). Aiming to tackle these two issues, in this paper, we present a robust activity recognition approach by extracting the intrinsic shared structures from activities to handle intra-class variability, and the approach is embedded into a semi-supervised learning framework by utilizing the learned correlations from both labeled and easily-obtained unlabeled data simultaneously. We use l2,1 minimization on both loss function and regularizations to effectively resist outliers in noisy sensor data and improve recognition accuracy by discerning underlying commonalities from activities. Extensive experimental evaluations on four community-contributed public datasets indicate that with little training samples, our proposed approach outperforms a set of classical supervised learning methods as well as those recently proposed semi-supervised approaches.
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    Conference Title
    UBICOMP'16: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING
    DOI
    https://doi.org/10.1145/2971648.2971701
    Subject
    Distributed computing and systems software not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/123941
    Collection
    • Conference outputs

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