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Simulation analysis of distance-aware graph-based semi-supervised learning

  • Yanyun Fan
  • , Lin Ma
  • , Yubin Xu
  • , Yang Cui
  • Harbin Institute of Technology
  • Ministry of Public Security of the People's Republic of China

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

Abstract

According to the problem of agglomeration effect of Graph-based Semi-Supervised Learning (G-SSL), this paper studies a Distance-aware Graph-based Semi-supervised Learning (DG-SSL) algorithm, which reduces the agglomeration effect of G-SSL, and holds smaller average estimation error. When compared with the K nearest neighbors (KNN) algorithm, moreover, the DG-SS algorithm can achieve better positioning result by using a small number of labeled samples. Simulation results show that the DG-SSL algorithm effectively resolve the problem of requiring enough labeled samples for Radio Map setup in indoor positioning algorithm. Thus, it reduces the workload and expenditure of establishing the Radio Map.

Original languageEnglish
Title of host publicationICEIEC 2015 - Proceedings of 2015 IEEE 5th International Conference on Electronics Information and Emergency Communication
EditorsVincent Tam, Zhu Wei, Li Wenzheng
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages55-58
Number of pages4
ISBN (Electronic)9781479972838
DOIs
StatePublished - 29 Sep 2015
Event5th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2015 - Beijing, China
Duration: 14 May 201516 May 2015

Publication series

NameICEIEC 2015 - Proceedings of 2015 IEEE 5th International Conference on Electronics Information and Emergency Communication

Conference

Conference5th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2015
Country/TerritoryChina
CityBeijing
Period14/05/1516/05/15

Keywords

  • Distance-aware
  • Radio Map
  • Semi-supervised
  • WLAN Indoor Positioning

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