SELM: Semi-supervised ELM with application in sparse calibrated location estimation

Neurocomputing

Volumn 74, Issue 16, 2011, Pages 2566-2572

  Liu, Junfa ^a   Chen, Yiqiang ^a   Liu, Mingjie ^a,b   Zhao, Zhongtang ^a,b  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Graph Laplacian; Location estimation; Semi supervised extreme learning machine; Sparse calibration

Indexed keywords

BACKPROPAGATION NETWORK; CALIBRATION DATA; CALIBRATION SAMPLES; COMPARATIVE EXPERIMENTS; EXTREME LEARNING MACHINE; GRAPH LAPLACIAN; LOCATION ESTIMATION; MACHINE LEARNING METHODS; REAL-TIME TRAINING; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING METHODS; SPARSE CALIBRATION; TRAINING AND TESTING; UNLABELED SAMPLES;

BACKPROPAGATION; CALIBRATION; INDUSTRIAL RESEARCH; LAPLACE TRANSFORMS; LEARNING SYSTEMS;

ESTIMATION;

ARTICLE; BACK PROPAGATION; CALIBRATION; GLOBAL POSITIONING SYSTEM; INFORMATION PROCESSING; INTERMETHOD COMPARISON; LEARNING ALGORITHM; LOCATION ESTIMATION; ONLINE SYSTEM; PRIORITY JOURNAL; SEMISUPERVISED EXTREME LEARNING MACHINE; STATISTICAL ANALYSIS;

EID: 80051590438     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.12.043     Document Type: Article

References (23)

1. Yang Q., Pan S.J., Zheng V.W. Estimating location using wi-fi. IEEE Intelligent Systems 2008, 23(1):8-13.
2. Liu H., Darabi H., Banerjee P., Liu J. Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man and Cybernetics 2007, 37(6):1067-1080.
3. Torres-Solis J., Falk T.H., Chau T. A review of indoor localization technologies: towards navigational assistance for topographical disorientation. Ambient Intelligence 2010, 51-84.
4. J. Krumm, J. Platt, Minimizing Calibration Effort For An Indoor 802.11 Device Location Measurement System, Technical Report, Microsoft Research, 2003.
5. Ocana M., Bergasa L.M., Sotelo M.A., Nuevo J., Flores R. Indoor robot localization system using wi-fi signal measure and minimizing calibration effort. IEEE ISIE 2005, 20:1545-1550.
6. Chai X., Yang Q. Reducing the calibration effort for location estimation using unlabeled samples. PerCom 2005, 95-104.
7. Pan J.J., Yang Q., Pan S.J. Online co-localization in indoor wireless networks by dimension reduction. Proceedings of the 22nd AAAI Conference on Artificial Intelligence 2007, 1102-1107.
8. Gwon Y., Jain R. Error characteristics and calibration-free techniques for wireless lan-based location estimation. Mobility Management and Wireless Access Protocols 2004, 2-9.
9. Varshavsky A., Pankratov D., Krumm J., de Lara E. Calibree: calibration-free localization using relative distance estimations. Pervasive 2008, 146-161.
10. Huang G.-B., Zhu Q.-Y., Siew C.-K. Extreme learning machine: a new learning scheme of feedforward neural networks. Proceedings of the International Joint Conference on Neural Networks 2004, 985-990.
11. Huang G.-B., Zhu Q.-Y., Siew C.-K. Extreme learning machine: theory and applications. Neurocomputing 2006, 70:489-501.
12. Huang G.-B., Chen L., Siew C.-K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks 2006, 17:879-892.
13. Huang G.-B., Chen L. Convex incremental extreme learning machine. Neurocomputing 2007, 70:3056-3062.
14. Huang G.-B., Chen L. Enhanced random search based incremental extreme learning machine. Neurocomputing 2008, 71:3460-3468.
15. Feng G., Huang G.-B., Lin Q., Gay R. Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Transactions on Neural Networks 2009, 20(8):1352-1357.
16. Yeu C.-W.T., Lim M.-H., Huang G.-B., Agarwal A., Ong Y.S. A new machine learning paradigm for terrain reconstruction. IEEE Geoscience and Remote Sensing Letters 2006, 3:382-386.
17. Liang N.-Y., Huang G.-B., Saratchandran P., Sundararajan N. A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks 2006, 17:1411-1423.
18. Li P.S.M.-B., Huang G.-B., Sundararajan N. Fully complex extreme learning machine. Neurocomputing 2005, 68:306-314.
19. Huang G.-B., Zhu Q.-Y., Mao K.Z., Siew C.-K., Saratchandran P., Sundararajan N. Can threshold networks be trained directly?. IEEE Transactions on Circuits and Systems-II 2006, 53:187-191.
20. Chen Y.-C., Chiang J.-R., hua Chu H., Huang P., Tsui A.W. Sensor-assisted Wi-Fi indoor location system for adapting to environmental dynamics. Sensor Networks 2005, 118-125.
21. Vapnik V. The Nature of Statistical Learning Theory 1995, Springer-Verlag.
22. Belkin M., Matveeva I., Niyogi P. Regularization and semi-supervised learning on large graphs. Proceedings of 17th Conference on Learning Theory (COLT) 2004.
23. Belkin M., Niyogi P., Sindhwani V. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. The Journal of Machine Learning Research 2006, 7(11):2399-2434.