Efficient kriging via fast matrix-vector products

IEEE Aerospace Conference Proceedings

Volumn , Issue , 2008, Pages

  Memarsadeghi, Nargess ^a   Raykar, Vikas C ^b   Duraiswami, Ramani ^c   Mount, David M ^c  

^a NASA GODDARD SPACE FLIGHT CENTER   (United States)

^b SIEMENS CORPORATE RESEARCH   (United States)

^c UNIVERSITY OF MARYLAND   (United States)

Author keywords

Approximate algorithms; Fast Gauss transform; Fast multipole methods; Geostatisties; Image fusion; Iterative methods; Kriging; Nearest neighbors

Indexed keywords

APPROXIMATE ALGORITHMS; CO-KRIGING; FAST GAUSS TRANSFORM; FAST MULTIPOLE METHODS; GEOSCIENCES; GEOSTATISTIES; KRIGING; LARGE DATA SETS; MATRIX-VECTOR PRODUCTS; NEAREST NEIGHBOR SEARCHING; NEAREST NEIGHBORS; ORDINARY KRIGING; REMOTELY SENSED DATA; SCATTERED DATA; SCATTERED DATA POINTS; TIME EFFICIENCIES;

EARTH SCIENCES; IMAGE FUSION; MATRIX ALGEBRA; NUMERICAL ANALYSIS; REMOTE SENSING; SPACE OPTICS; VECTORS;

ITERATIVE METHODS;

EID: 49349097343     PISSN: 1095323X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AERO.2008.4526433     Document Type: Conference Paper

References (29)

1. I. Amidror, "Scattered data interpolation methods for electronic imaging systems: A survey," Journal of Electronic Imaging, vol. 11, no. 2, pp. 157-176, April 2002.
2. P. Alfeld, "Scattered data interpolation in three or more variables." Mathematical methods in computer aided geometric design, pp. 1-33, 1989.
3. E. H. Isaaks and R. M. Srivastava, An Introduction to Applied Geostatistics. New York, Oxford: Oxford University Press. 1989.
4. A. G. Journel and C. J. Huijbregts, Mining Geostatistics. New York: Academic Press Inc, 1978.
5. P. Goovaerts, Geostatistics for Natural Resources Evaluation. New York, Oxford: Oxford University Press, 1997.
6. D. L. Hall, Mathematical techniques in multisensor data fusion. Norwood: Artech House Inc, 1992.
7. Y. Zhang, "Understanding image fusion," Photogram-metric Engineering and Remote Sensing, pp. 657-661, June 2004.
8. N. Memarsadeghi, J. L. Moigne, D. M. Mount, and J. Morisette, "A new approach to image fusion based on cokriging," in. the Eighth International Conference on Information Fusion, vol. 1, July 2005, pp. 622-629.
9. N. Memarsadeghi, J. L. Moigne, and D. M. Mount, "Image fusion using cokriging," in IEEE International Geoscience and Remote Sensing Symposium (IGARSS'06), July 2006, pp. 2518-2521.
10. T. H. Meyer, "The discontinuous nature of kriging interpolation for digital terrain modeling," Cartography and Geographic Information Science., vol. 31, no. 4, pp. 209-216, 2004.
11. R. Furrer, ML G. Genton, and I). Nychka, "Covariance tapering for interpolation of large spatial datasets," Journal of Computational and Graphical Statistics, vol. 15, no. 3, pp. 502-523, September 2006.
12. N. Memarsadeghi and. D. M. Mount, "Efficient implementation of an optimal interpolator for large spatial, data sets," in Proceedings of the International Conference on Computational Science (ICCS'07), ser. Lecture Notes in Computer Science, vol. 4488. Sprmger-Yerlag, May 2007, pp. 503-510.
13. L. Greengard. and V Rokhlin, "A fast algorithm for particle simulation," Journal of Computational Physics, vol. 73, no. 2, pp. 325-348, 1987.
14. L. Greengard, "The rapid evaluation of potential fields in particle systems," Ph.D. dissertation, Yale, NYU, 1987.
15. C. Yang, R. Duraiswami, and L. Davis, "Efficient kernel machines using the improved fast Gauss transform." in Advances in Neural Information Processing Systems, L. K. Saul, Y. Weiss, and L. Bottou, Eds., vol.. 17. MIT Press, 2005, pp. 1561-1568.
16. Y C. Raykar, C. Y"ang, R. Duraiswami, and N. Gumerov, "Fast computation of sums of Gaussians in high dimensions," Department of Computer Science, University of Maryland, College Park, MD, 20742, Tech. Rep., 2005, CS-TR-4767.
17. C. C. Paige and M. A. Saunders, "Solution of sparse indefinite systems of linear equations," SIAM Journal on Numerical Analysis, vol. 12, no. 4, pp. 617-629, September 1975.
18. S. D. Billings, R. K. Beatson, and G. N. Newsam, "Interpolation of geophysical data using continuous global surfaces," Geophysics, vol.. 67, no. 6, pp. 1810-1822, November-December 2002.
19. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. MIT Press, 2006.
20. G. Christakos, "On the problem of permissible co-variance and variogram models," Water Resources Research, vol. 20, no. 2, pp. 251-265, February 1984.
21. Y. Saad, Iterative methods for sparse linear systems. SIAM, 2003.
22. S. G. Nash and A. Sofer. Linear and Nonlinear Programming, McGraw-Hill Companies, 1996.
23. L. Greengard and J. Strain, "The fast Gauss transform," SIAM Journal of Scientific and Statistical Computing., vol. 12, no. 1, pp. 79-94, 1991.
24. V. C. Raykar and R. Duraiswami, Large Scale Kernel Machines. MIT Press, 2007, ch. The Improved Fast Gauss Transform with applications to machine learning.
25. T. Feder and D. H. Greene, "Optimal algorithms for approximate clustering", in Proc. 20th Annual ACM Symposium on Theory of Computing, 1988, pp. 434-144.
26. V. C. Raykar, "Scalable machine learning for massive datasets: Fast summation algorithms," Ph.D. dissertation, University of Maryland, College Park, MD, 20742, March 2007.
27. D. M. Mount and S. Arya, "ANN: A library for approximate nearest neighbor searching," http://www.cs.umd.edu/∼mount/ANN/, May 2005.
28. N. Remy, "GsTL: The Geostatistical Template Library in C++," Master's thesis, Department of Petroleum Engineering of Stanford University, March 2001.
29. N. Memarsadeghi, "Efficient algorithms for clustering and interpolation of large spatial data sets," Ph.D. dissertation, University of Maryland, College Park, MD, 20742, April 2007.