Indoor location estimation with reduced calibration exploiting unlabeled data via hybrid generative/discriminative learning

Robin Wentao Ouyang, Albert Kai Sun Wong, Chin Tau Lea, Mung Chiang

Research output: Contribution to journalArticle

62 Scopus citations

Abstract

For indoor location estimation based on wireless local area networks fingerprinting, how to reduce the offline calibration effort while maintaining high location estimation accuracy is of major concern. In this paper, a hybrid generative/discriminative semi-supervised learning algorithm is proposed that utilizes a large number of unlabeled samples to supplement a small number of labeled samples. This hybrid method allows us to combine the modeling power and flexibility of generative models with the superior performance of discriminative approaches. Other related issues, such as learning efficiency enhancement and distribution estimation smoothing, are also discussed. Extensive experimental results show that our proposed method can effectively reduce the calibration effort and exhibit superior performance in terms of localization accuracy and robustness.

Original languageEnglish (US)
Article number6018966
Pages (from-to)1613-1626
Number of pages14
JournalIEEE Transactions on Mobile Computing
Volume11
Issue number11
DOIs
StatePublished - Oct 1 2012

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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