Indoor Location Estimation with Reduced Calibration Exploiting Unlabeled Data via Hybrid Generative/Discriminative Learning
Issue No. 11 - Nov. (2012 vol. 11)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TMC.2011.193
Robin Wentao Ouyang , Hong Kong University of Science and Technology, Hong Kong
Albert Kai-Sun Wong , Hong Kong University of Science and Technology, Hong Kong
Chin-Tau Lea , Hong Kong University of Science and Technology, Hong Kong
Mung Chiang , Princeton University, Princeton
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.
Estimation, Accuracy, Probabilistic logic, Indexes, Calibration, Data models, Kernel, least square support vector machine, Indoor location estimation, wireless local area network, hybrid semi-supervised learning, naive Bayes, expectation maximization, fisher kernel
C. Lea, A. K. Wong, R. W. Ouyang and M. Chiang, "Indoor Location Estimation with Reduced Calibration Exploiting Unlabeled Data via Hybrid Generative/Discriminative Learning," in IEEE Transactions on Mobile Computing, vol. 11, no. , pp. 1613-1626, 2012.