Robust extreme learning machine for regression problems with its application to wifi based indoor positioning system

Xiaoxuan Lu, Yushen Long, Han Zou, Chengpu Yu, Lihua Xie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose two kinds of robust extreme learning machines (RELMs) based on the close-to-mean constraint and the small-residual constraint respectively to solve the problem of noisy measurements in indoor positioning systems (IPSs). We formulate both RELMs as second order cone programming problems. The fact that feature mapping in ELM is known to users is exploited to give the needed information for robust constraints. Real-world indoor localization experimental results show that, the proposed algorithms can not only improve the accuracy and repeatability, but also reduce the deviations and worst case errors of IPSs compared with basic ELM and OPT-ELM based IPSs.
Original languageEnglish
Title of host publication2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-6
Number of pages6
ISBN (Electronic)978-1-4799-3694-6
DOIs
Publication statusPublished - 20 Nov 2014
Event24th IEEE International Workshop on Machine Learning for Signal Processing - Reims, France
Duration: 21 Sep 201424 Sep 2014

Publication series

Name
PublisherIEEE
ISSN (Print)1551-2541
ISSN (Electronic)2378-928X

Workshop

Workshop24th IEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMSLP 2014
Country/TerritoryFrance
CityReims
Period21/09/1424/09/14

Keywords

  • Robust extreme learning machine
  • Indoor positioning system
  • Second order cone programming

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