Regression in sensor networks: Training distributively with alternating projections

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5910, Issue , 2005, Pages 1-15

  Predd, Joel B ^a   Kulkarni, Sanjeev R ^a   Vincent Poor, H ^a  

^a Princeton University   (United States)

Author keywords

Distributed estimation; Distributed learning, nonparametric; Kernel methods; Regression, alternating projections; Wireless sensor networks

Indexed keywords

ALGORITHMS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBLEM SOLVING; REGRESSION ANALYSIS; SENSORS;

DISTRIBUTED ESTIMATION; DISTRIBUTED LEARNING, NONPARAMETRIC; KERNEL METHODS; REGRESSION, ALTERNATING PROJECTION; WIRELESS SENSOR NETWORKS;

SIGNAL PROCESSING;

EID: 30644465872     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.620194     Document Type: Conference Paper

References (30)

1. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A survey on sensor networks," IEEE Communications Magazine 40(8), pp. 102-114, 2002.
2. V. Delouille, R. Neelamani, and R. Baraniuk, "Robust distributed estimation in sensor networks using the embedded polygons algorithm," in IPSN'04: Proceedings of the Third International Symposium on Information Processing in Sensor Networks, pp. 405-413, ACM Press, (New York). 2004.
3. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, 1988.
4. Y. Weiss and W. T. Freeman, "Correctness of belief propagation in Gaussian graphical models of arbitrary topology," Neural Computation 13, pp. 2173-2200, 2001.
5. E. Sudderth, M. J. Wainwright, and A. S. Willsky, "Embedded trees: Estimation of Gaussian processes on graphs with cycles," tech. rep., MIT, 2003.
6. K. Plarre and P. R. Kumar, "Extended message passing algorithm for inference in loopy Gaussian graphical models," Ad Hoc Networks 2, pp. 153-169, 2004.
7. R. D. Nowak, "Distributed EM algorithms for density estimation and clustering in sensor networks," IEEE Transactions on Signal Processing 51, pp. 2245-2253, August 2003.
8. D. Blatt and A. Hero, "Distributed maximum likelihood estimation for sensor networks," in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pp. 929-932, May 2004.
9. M. Rabbat and R. Nowak, "Distributed optimization in sensor networks," in IPSN'04: Proceedings of the Third International Symposium on Information Processing in Sensor Networks, pp. 20-27, ACM Press, (New York), 2004.
10. S.-H. Son, M. Chiang, S. R. Kulkarni, and S. C. Schwartz, "The value of clustering in distributed estimation for sensor networks," in Proceedings of the IEEE International Conference on Wireless Networks, Communications, and Mobile Computing (WirelessCom 05), (Maui, Hawaii), June 2005.
11. A. Ribeiro and G. B. Giannakis, "Bandwidth-constrained distributed estimation for wireless sensor networks, part I: Gaussian case," submitted to IEEE Transaction on Signal Processing, July 2004.
12. Z. Q. Luo, "Universal decentralized estimation in a bandwidth constraint sensor network," submitted to IEEE Transactions on Information Theory, 2004.
13. J. B. Predd, S. R. Kulkarni, and H. V. Poor, "Consistency in a model for distributed learning with specialists," in Proceedings of the 2004 IEEE International Symposium on Information Theory, (Chicago, IL), June 2004.
14. J. B. Predd, S. R. Kulkarni, and H. V. Poor, "Consistency in models for distributed learning under communication constraints," submitted to IEEE Trans. on Information Theory, 2005.
15. B. Schölkopf and A. Smola, Learning with Kernels, MIT Press, Cambridge, MA, 1st ed., 2002.
16. Y. Censor and S. A. Zenios, Parallel Optimization: Theory, Algorithms, and Applications, Oxford, New York, 1997.
17. C. Guestrin, P. Bodi, R. Thibau, M. Paskin, and S. Madde, "Distributed regression: an efficient framework for modeling sensor network data," in IPSN'04: Proceedings of the Third International Symposium on Information Processing in Sensor Networks, pp. 1-10, ACM Press, (New York), 2004.
18. R. L. Dykstra, "An algorithm for restricted least squares regression," Journal of the American Statistical Association 78(384), pp. 837-842, 1983.
19. H. Stark, ed., Image Recovery: Theory and Application, Academic Press, Orlando, FL, 1987.
20. A. O. Hero and D. Blatt, "Sensor network source localization via projection onto convex sets (POCS)," in Proceedings of the IEEE International Conference on Acoustics. Speech, and Signal Processing (ICASSP 05), 5, pp. 1065-1068, (Philadelphia, PA), March 2005.
21. G. Wahba, "An introduction to model buidling with reproducing kernel Hubert spaces," Tech. Rep. TR-1020, Statistics Department, University of Wisconsin-Madison, 2000.
22. S. M. Aji and R. J. McEliece, "The generalized distributive law," IEEE Transactions on Information Theory 46, pp. 325-343, March 2000.
23. H. H. Bauschke and J. M. Borwein, "On projection algorithms for solving convex feasibility problems," SIAM Review 38, pp. 367-426, September 1996.
24. T. Kailath and H. V. Poor, "Detection of stochastic processes, " IEEE Transactions on Information Theory 44, pp. 2230-2259, October 1998.
25. L. Györfi, M. Kohler, A. Krzyzak, and H. Walk. A Distribution-Free Theory of Nonparametric Regression, Springer, 2002.
26. G. Kimeldorf and G. Wahba. "Some results on Tchebycheffian spline functions," Journal of Mathematical Analysis and Applications 33, pp. 82-95, 1971.
27. S. N. Simic, "A learning theory approach to sensor networks," IEEE Pervasive Computing 2(4), pp. 44-49, 2003.
28. X. Nguyen, M. J. Wainwright, and M. I. Jordan, "Decentralized detection and classification using kernel methods," in Proceedings of the Twenty-first International Conference on Machine Learning, (Banff, Canada), 2004.
29. J. B. Predd, D. Osherson, S. R. Kulkarni, and H. V. Poor, "Efficient algorithms for aggregating expert opinion," preprint, 2005.
30. D. Osherson and M. Y. Vardi, "Aggregating disparate esimtates of chance," to appear in Games and Economic Behavior, 2005.