Learning from less for better: semi-supervised activity recognition via shared structure discovery
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Date
2016
Authors
Yao, L.
Nie, F.
Sheng, Q.
Gu, T.
Li, X.
Wang, S.
Editors
Lukowicz, P.
Krüger, A.
Bulling, A.
Lim, Y.-K.
Patel, S.
Krüger, A.
Bulling, A.
Lim, Y.-K.
Patel, S.
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Conference paper
Citation
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016 / Lukowicz, P., Krüger, A., Bulling, A., Lim, Y.-K., Patel, S. (ed./s), pp.13-24
Statement of Responsibility
Lina Yao, Feiping Nie, Quan Z. Sheng, Tao Gu, Xue Li, and Sen Wang
Conference Name
2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) (12 Sep 2016 - 16 Sep 2016 : Heidelberg, Germany)
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 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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©2016 ACM.