Distributionally robust semi-supervised learning for people-centric sensing

Kaixuan Chen, Lina Yao, Dalin Zhang, Xiaojun Chang, Guodong Long, Sen Wang

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

14 Citations (Scopus)

Abstract

Semi-supervised learning is crucial for alleviating la-belling burdens in people-centric sensing. However, human-generated 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.

Original languageEnglish
Title of host publicationProceedings of AAAI19-Thirty-Third AAAI conference on Artificial Intelligence
EditorsPascal Van Hentenryck, Zhi-Hua Zhou
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages3321-3328
Number of pages8
ISBN (Electronic)9781577358091
DOIs
Publication statusPublished - 2019
EventAAAI Conference on Artificial Intelligence 2019 - Honolulu, United States of America
Duration: 27 Jan 20191 Feb 2019
Conference number: 33rd
https://aaai.org/Conferences/AAAI-19/

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Number1
Volume33
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

ConferenceAAAI Conference on Artificial Intelligence 2019
Abbreviated titleAAAI 2019
Country/TerritoryUnited States of America
CityHonolulu
Period27/01/191/02/19
Internet address

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