Learning from less for better: semi-supervised activity recognition via shared structure discovery

Lina Yao, Feiping Nie, Quan Z. Sheng, Tao Gu, Xue Li, Sen Wang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

27 Citations (Scopus)

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 semisupervised approaches.

Original languageEnglish
Title of host publicationUbiComp 2016
Subtitle of host publicationProceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery, Inc
Pages13-24
Number of pages12
ISBN (Electronic)9781450344616
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, Germany
Duration: 12 Sep 201616 Sep 2016

Other

Other2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
CountryGermany
CityHeidelberg
Period12/09/1616/09/16

Keywords

  • activity recognition
  • shared structure analysis
  • semi-supervised learning
  • optimization

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