Transfer learning on high variety domains for activity recognition

Josh Jia Ching Ying, Bo Hau Lin, Vincent S. Tseng, Sun Yuan Hsieh

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

The research topic on transfer learning task has attracted a lot of attentions in recent years due to the wide applications. Although a number of transfer learning techniques have been developed, basically they were designed in the manner of learning and transferring among multiple source domains and it was assumed that the source domains and target domain share the same feature space. However, with the high variety issue under big data environments, this assumption violates the scenario of many real-world applications like activity recognition. In this paper, we propose a novel approach for transfer learning on activity recognition with the new concept of transfer learning on high variety domains. The core idea of our transferring model is based on theoretical statistic hypothesis tests, Kolmogorov-Smirnov test and x2goodness of fit test, which evaluate how well a domain is covered by another domain based on similarity between each pair of features. Through comprehensive evaluations by experiments, our proposal is shown to deliver excellent effectiveness and substantially outperform state-of-the-art multiple source domain transfer learning methods. To our best knowledge, this is the first work that explores the problem of transfer learning on high variety domains for activity recognition with promising potential in wide applications.

原文English
主出版物標題Proceedings of the ASE BigData and SocialInformatics 2015, ASE BD and SI 2015
發行者Association for Computing Machinery
ISBN(電子)9781450337359
DOIs
出版狀態Published - 2015 十月 7
事件ASE BigData and SocialInformatics, ASE BD and SI 2015 - Kaohsiung, Taiwan
持續時間: 2015 十月 72015 十月 9

出版系列

名字ACM International Conference Proceeding Series
07-09-Ocobert-2015

Other

OtherASE BigData and SocialInformatics, ASE BD and SI 2015
國家Taiwan
城市Kaohsiung
期間15-10-0715-10-09

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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    Ying, J. J. C., Lin, B. H., Tseng, V. S., & Hsieh, S. Y. (2015). Transfer learning on high variety domains for activity recognition. 於 Proceedings of the ASE BigData and SocialInformatics 2015, ASE BD and SI 2015 [a37] (ACM International Conference Proceeding Series; 卷 07-09-Ocobert-2015). Association for Computing Machinery. https://doi.org/10.1145/2818869.2818890