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Type: Conference paper
Title: Learning from less for better: semi-supervised activity recognition via shared structure discovery
Author: Yao, L.
Nie, F.
Sheng, Q.
Gu, T.
Li, X.
Wang, S.
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
Publisher: Association for Computing Machinery
Issue Date: 2016
ISBN: 9781450344616
Conference Name: 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) (12 Sep 2016 - 16 Sep 2016 : Heidelberg, Germany)
Statement of
Lina Yao, Feiping Nie, Quan Z. Sheng, Tao Gu, Xue Li, and Sen Wang
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.
Keywords: Activity recognition; shared structure analysis; semi-supervised learning; optimization
Rights: ©2016 ACM.
RMID: 0030057199
DOI: 10.1145/2971648.2971701
Appears in Collections:Computer Science publications

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