Semi-supervised deep extreme learning machine for Wi-Fi based localization

Neurocomputing

Volumn 166, Issue , 2015, Pages 282-293

  Gu, Yang ^a,b,c   Chen, Yiqiang ^a,b   Liu, Junfa ^a,b   Jiang, Xinlong ^a,b,c  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b BEIJING KEY LABORATORY OF MOBILE COMPUTING AND PERVASIVE DEVICE   (China)

^c UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Deep learning; Extreme Learning Machine (ELM); Semi supervised learning; Wi Fi indoor localization

Indexed keywords

DATA ACQUISITION; DEEP LEARNING; INDOOR POSITIONING SYSTEMS; KNOWLEDGE ACQUISITION; LABELED DATA; SEMI-SUPERVISED LEARNING; WI-FI; WIRELESS LOCAL AREA NETWORKS (WLAN);

EFFECTIVE APPROACHES; EXTRACTION PROCEDURE; EXTREME LEARNING MACHINE; INDOOR ENVIRONMENT; INDOOR LOCALIZATION; LOCALIZATION PERFORMANCE; SEMI-SUPERVISED; WIRELESS SIGNALS;

LEARNING SYSTEMS;

ARTICLE; CALIBRATION; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; DATA PROCESSING; DEEP LEARNING; ENVIRONMENT; EXTREME MACHINE LEARNING; INDOOR ENVIRONMENT; INTERNET; MACHINE LEARNING; MEASUREMENT ACCURACY; PRIORITY JOURNAL; SEMI SUPERVISED LEARNING; UNLABELED DATA; WI FI;

EID: 84931573903     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.04.011     Document Type: Article

References (42)

1. Bahl P., Padmanabhan V.N. RADAR: An in-building RF-based user location and tracking system. in Proc. IEEE INFOCOM 2000, 775-784.
2. Zheng V., Xiang E., Yang Q., Shen D. Transferring localization models over time. in Proc. AAAI Conference on Artificial Intelligence 2008, 1421-1426.
3. Yang Q., Pan S.J., Zheng V.W. Estimating location using Wi-Fi. IEEE Intelligent Systems 2008, 23(1):8-13.
4. Moraes L., Nunes B. Calibration-free WLAN location system based on dynamic mapping of signal strength. in MobiWAC 2006, 92-99.
5. J. Ledlie, J. Park, D. Curtis and et al. "Molé: A scalable, user-generated WiFi positioning engine," in 2011 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2011, pp.1-10.
6. Ekahau positioning engine. Available from: . http://ekahau.com.
7. Chatzimilioudis G., Konstantinidis A., Laoudias C., et al. Crowdsourcing with smartphones. in IEEE Internet Computing 2012, 16(5):36-44.
8. Zeinalipour-Yazti D., Laoudias C., Costa C., et al. Crowdsourced trace similarity with smartphones. in IEEE Trans. on Knowledge and Data Engineering 2013, 25(6):1240-1253.
9. C. Laoudias, D. Zeinalipour-Yazti, C. Panayiotou, "Crowdsourced Indoor Localization for Diverse Devices through Radiomap Fusion," in International Conference on Indoor Positioning and Indoor Navigation, 2013, pp.25- 28.
10. Hinton G.E., Salakhutdinov.R.R. Reducing the Dimensionality of Data with Neural Networks. in Science 2006, 313(5786):504-507.
11. Haeberlen A., Flannery E., Ladd A.M., Rudys A., Wallach D.S., Kavraki L.E. Practical robust localization over large-scale 802.11 wireless networks. in ACM MOBICOM'04 2004, 70-84. Philadelphia.
12. K. Kaemarungsi, "Distribution of WLAN received signal strength indication for indoor location determination," in 1st Int. Symposium on Wireless Pervasive Computing, Phuket, 2006.
13. K. Kaemarungsi and P. Krishnamurthy, "Properties of indoor received signal strength for WLAN location fingerprinting," in Proc. the First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous'04), 2004, pp. 14-23.
14. Chen Y., Chiang J., Chu H., Huang P., Tsui A. Sensor-sssisted Wi-Fi indoor location system for adapting to environmental dynamics. in MSWiM '05 2005, 118-125. Quebec.
15. P. Bahl,V.N. Padmanabhan, "Enhancements of the RADAR user location and tracking system," Microsoft Corp., Redmond, WA, Tech. Rep. MSR-TR-2000-12, Feb.2000.
16. Zhu J., Zhao H., Sun P., Bi Y. Equilateral triangle localization algorithm based on average RSSI. in Journal of Northeast University (Natural Science) 2007, 28(8):1094-1097. In Chinese.
17. Fang S., Lin T. Principal component localization in Indoor WLAN environments. in IEEE Transaction on Mobile Computing 2012, 11(1):100-110.
18. M. Youssef, A. Agrawala, and A.U. Shankar, "WLAN location determination via clustering and probability distributions," in Proc. the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003), 2003, pp. 143-150.
19. Chen Y., Yin J., Chai X., Yang Q. Power-efficient access-point selection for indoor location estimation. in IEEE Trans. Knowledge and Data Eng. 2006, 18(7):877-888.
20. M. Kjaergaard, C. Munk, "Hyperbolic location fingerprinting: a calibration-free solution for handling differences in signal strength," in Proc. IEEE International Conference on Pervasive Computing, 2008. 110-116.
21. Fet N., Handte M., Marrón P.J. A model for WLAN signal attenuation of the human body. in Ubicomp 2013, ACM 2013, 499-508.
22. Pan J.J., Pan S.J., Yin J., Ni L.M., Yang Q. Tracking mobile users in wireless networks via semi-supervised co-localization. in IEEE Trans Pattern Anal Mach Intell 2012, 34(3):587-600.
23. T. Pulkkinen, T. Roos, and P. Myllymaki, "Semi-supervised learning for wlan positioning," in Proc. of 21th Int. Conf. Artificial Neural Networks, Berlin, Heidelberg, 2011, pp. 355-362.
24. Zhang Y., Zhi X. Indoor positioning algorithm based on semi-supervised learning. in Computer Engineering 2010, 36(17):277-279. In Chinese.
25. Lin Q., Zhao F., Luo H., Kang Y. A wireless localization algorithm based on spectral decomposition of the graph laplacian. in Acta Automatica Sinica 2011, 37(3):316-321. In Chinese.
26. J. Liu, Y. Chen, M. Liu, Z. Zhao, "SELM: Semi-supervised ELM with application in sparse calibrated location estimation," in Neurocomputing, vol.74, no.16, pp. 2566-2572, 2011.
27. Chapelle O., Schölkopf B., Zien A. Semi-supervised learning 2006, 1-12. MIT Press, Massachusetts.
28. Hinton G., Osindero S., Teh Y. A fast learning algorithm for deep belief Nets. in Neural Computation 2006, 18(7):1527-1554.
29. Bengio Y. Learning deep architectures for AI. in Foundations and Trends in Machine Learning 2009, 2(1):1-127.
30. Quoc V., Ranzato M., Monga R., Devin M., Chen K., Corrado G., Dean J., Ng A. Building high-level features using large scale unsupervised learning 2012, ACM Press, Edinburgh, in Proc. 29th Int. Conf. Machine Learning.
31. Morgan N. Deep and wide: multiple layers in automatic speech recognition. in IEEE Transactions on Audio, Speech, and Language Processing 2012, 20(1):7-13.
32. Hinton G., Salakhutdinov R. Reducing the dimensionality of data with neural networks. Science 2006, 313(5786):504-507.
33. K. Yu, Y. Lin , J. Lafferty, "Learning image representations from the pixel level via hierarchical sparse coding,". in Proc. IEEE Conf. Comput. Vis. Pattern Recognit, 2011, pp.1713-1720.
34. Lee H., Battle A., Raina R., Ng A. Efficient sparse coding algorithms. in Proc. Neural Information Processing Systems 2007, 801-808.
35. http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial.
36. Huang G.B., Zhu Q.Y., Siew C.K. Extreme learning machine: A new learning scheme of feedforward neural networks. in Int. Conf. Neural Networks 2004, 985-990.
37. Huang G.B., Siew C.K. Extreme learning machine: RBF network case. in Int. Conf. on Control, Automation, Robotics and Vision (ICARCV) 2004, 1029-1036.
38. Huang G.B., Siew C.K. Extreme learning machine with randomly assigned RBF kernels. in International Journal of Information Technology 2005, 11(1):16-24.
39. Huang G.B., Zhu Q.Y., Siew C.K. Extreme learning machine: Theory and applications. in Neurocomputing 2006, 70(1-3):489-501.
40. Kasun L.L.C., Zhou H., Huang G.-B., Vong C.M. RepresentationalLearningwithExtremeLearningMachineforBigData. in IEEEIntelligentSystems 2013, 28(6):31-34.
41. L.B.D. Mundo, R.L.D. Ansay, C.A.M. Festin, and R.M. Ocampo, "A comparison of Wireless Fidelity (Wi-Fi) fingerprinting techniques," in 2011 International Conference on ICT Convergence (ICTC), 2011, pp.20-25.
42. Chang C.C., Lin C.J. LIBSVM: a library for support vector machines. in ACM Transactions on Intelligent Systems and Technology 2011, 2(3).