The Community for Technology Leaders
RSS Icon
Issue No.11 - Nov. (2012 vol.11)
pp: 1613-1626
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
Robin Wentao Ouyang, Albert Kai-Sun Wong, Chin-Tau Lea, Mung Chiang, "Indoor Location Estimation with Reduced Calibration Exploiting Unlabeled Data via Hybrid Generative/Discriminative Learning", IEEE Transactions on Mobile Computing, vol.11, no. 11, pp. 1613-1626, Nov. 2012, doi:10.1109/TMC.2011.193
[1] M. Rodriguez, J. Favela, E. Martinez, and M. Munoz, "Location-Aware Access to Hospital Information and Services," IEEE Trans. Information Technology in Biomedicine, vol. 8, no. 4, pp. 448-455, Dec. 2004.
[2] H. Harroud, M. Ahmed, and A. Karmouch, "Policy-Driven Personalized Multimedia Services for Mobile Users," IEEE Trans. Mobile Computing, vol. 2, no. 1, pp. 16-24, Jan.-Mar. 2003.
[3] C. Patterson, R. Muntz, and C. Pancake, "Challenges in Location-Aware Computing," IEEE Pervasive Computing, vol. 2, no. 2 pp. 80-89, Apr.-June 2003.
[4] A.K.-S. Wong, T.K. Woo, A.-L. Lee, X. Xiao, V.-H. Luk, and K.W. Cheng, "An AGPS-Based Elderly Tracking System," Proc. First Int'l Conf. Ubiquitous and Future Networks (ICUFN '09), pp. 100-105, June 2009.
[5] P. Bahl and V. Padmanabhan, "RADAR: An In-Building RF-Based User Location and Tracking System," Proc. IEEE INFOCOM, vol. 2, pp. 775-784, 2002.
[6] J. Pan, J. Kwok, Q. Yang, and Y. Chen, "Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing," IEEE Trans. Knowledge and Data Eng., vol. 18, no. 3, pp. 1181-1193, Sept. 2006.
[7] A. Kushki, K. Plataniotis, and A. Venetsanopoulos, "Kernel-Based Positioning in Wireless Local Area Networks," IEEE Trans. Mobile Computing, vol. 6, no. 6, pp. 689-705, June 2007.
[8] T. Roos, P. Myllymäki, H. Tirri, P. Misikangas, and J. Sievänen, "A Probabilistic Approach to WLAN User Location Estimation," Int'l J. Wireless Information Networks, vol. 9, no. 3, pp. 155-164, 2002.
[9] Z. Wu, C. Li, J. Ng, and K. Leung, "Location Estimation via Support Vector Regression," IEEE Trans. Mobile Computing, vol. 6, no. 3, pp. 311-321, Mar. 2007.
[10] Y. Chen, Q. Yang, J. Yin, and X. Chai, "Power-Efficient Access-Point Selection for Indoor Location Estimation," IEEE Trans. Knowledge and Data Eng., vol. 18, no. 7, pp. 877-888, July 2006.
[11] R.W. Ouyang, A.K. Wong, and C.T. Lea, "Received Signal Strength-Based Wireless Localization via Semidefinite Programming: Noncooperative and Cooperative Schemes," IEEE Trans. Vehicular Technology, vol. 59, no. 3, pp. 1307-1318, Mar. 2010.
[12] X. Li and K. Pahlavan, "Super-Resolution TOA Estimation with Diversity for Indoor Geolocation," IEEE Trans. Wireless Comm., vol. 3, no. 1, pp. 224-234, Jan. 2004.
[13] C. Yang, Y. Huang, and X. Zhu, "Hybrid TDOA/AOA Method for Indoor Positioning Systems," Proc. Institution of Eng. and Technology Seminar Location Technologies, pp. 1-5, 2008.
[14] M. Kjærgaard, "A Taxonomy for Radio Location Fingerprinting," Proc. Int'l Conf. Location and Context-Awareness, pp. 139-156, 2007.
[15] C. Bishop et al., Pattern Recognition and Machine Learning. Springer, 2006.
[16] E. Alpaydin, Introduction to Machine Learning, second ed. MIT Press, 2010.
[17] A. Jordan, "On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes," Proc. Conf. Advances in Neural Information Processing Systems, vol. 2, pp. 841-848, 2002.
[18] A. Dempster, N. Laird, and D. Rubin, "Maximum Likelihood from Incomplete Data via the EM Algorithm," J. Royal Statistical Soc. Series B (Methodological), vol. 39, no. 1, pp. 1-38, 1977.
[19] T. Jaakkola and D. Haussler, "Exploiting Generative Models in Discriminative Classifiers," Proc. Advances in Neural Information Processing Systems, pp. 487-493, 1999.
[20] J. Suykens and J. Vandewalle, "Least Squares Support Vector Machine Classifiers," Neural Processing Letters, vol. 9, no. 3, pp. 293-300, 1999.
[21] M. Youssef et al., "WLAN Location Determination via Clustering and Probability Distributions," Proc. First IEEE Int'l Conf. Pervasive Computing and Comm. (PerCom '03), pp. 143-150, 2003.
[22] M. Brunato et al., "Transparent Location Fingerprinting for Wireless Services," Proc. Med-Hoc-Net, vol. 2002, 2002.
[23] J. Krumm and J. Platt, "Minimizing Calibration Effort for an Indoor 802.11 Device Location Measurement System," Microsoft Research, Nov. 2003.
[24] X. Chai and Q. Yang, "Reducing the Calibration Effort for Probabilistic Indoor Location Estimation," IEEE Trans. Mobile Computing, vol. 6, no. 6, pp. 649-662, June 2007.
[25] D. Madigan, E. Einahrawy, R. Martin, W. Ju, P. Krishnan, and A. Krishnakumar, "Bayesian Indoor Positioning Systems," Proc. IEEE INFOCOM, vol. 2, pp. 1217-1227, 2005.
[26] S. Liu, H. Luo, and S. Zou, "A Low-Cost and Accurate Indoor Localization Algorithm Using Label Propagation Based Semi-Supervised Learning," Proc. Fifth Int'l Conf. Mobile Ad-Hoc and Sensor Networks, pp. 108-111, 2009.
[27] X. Zhu and Z. Ghahramani, "Learning from Labeled and Unlabeled Data with Label Propagation," Technical Report CMUCALD-02-107, School of Computer Sciences, Carnegie Mellon Univ., 2002.
[28] S. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice-Hall, 1993.
[29] H. Hashemi, "The Indoor Radio Propagation Channel," Proc. IEEE, vol. 81, no. 7, pp. 943-968, July 2002.
[30] R.W. Ouyang, A. Wong, and K. Woo, "Indoor Localization via Discriminatively Regularized Least Square Classification," Int'l J. Wireless Information Networks, vol. 18, pp. 57-72, , 2011.
[31] T.M. Mitchell, "Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression," Machine Learning, McGraw Hill, 2005.
[32] T. Roos, P. Myllymaki, and H. Tirri, "A Statistical Modeling Approach to Location Estimation," IEEE Trans. Mobile Computing, vol. 1, no. 1, pp. 59-69, Jan.-Mar. 2002.
[33] K. Nigam, A. McCallum, S. Thrun, and T. Mitchell, "Text Classification from Labeled and Unlabeled Documents Using EM," Machine Learning, vol. 39, no. 2, pp. 103-134, 2000.
[34] T. Hofmann, "Learning the Similarity of Documents: An Information-Geometric Approach to Document Retrieval and Categorization," Proc. Neural Information Processing Systems, 2000.
[35] J. Ye and T. Xiong, "SVM Versus Least Squares SVM," Proc. Int'l Conf. Artificial Intelligence and Statistics, pp. 640-647, 2007.
[36] B. Schölkopf and A. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2002.
[37] K. Pahlavan and A. Levesque, Wireless Information Networks. Wiley, 1995.
43 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool