Indexing the earth mover's distance using normal distributions

Proceedings of the VLDB Endowment

Volumn 5, Issue 3, 2011, Pages 205-216

  Ruttenberg, Brian E ^a   Singh, Ambuj K ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; NEAREST NEIGHBOR SEARCH; NORMAL DISTRIBUTION; QUERY PROCESSING; STOCHASTIC SYSTEMS;

EARTH MOVER'S DISTANCE; HETEROGENEOUS DATA; K-NEAREST NEIGHBORS; PROJECTION VECTORS; STOCHASTIC DOMINANCE; TRANSPORTATION PROBLEM; UNCERTAIN DATA SETS; UNCERTAIN DATABASE;

PROBABILITY DISTRIBUTIONS;

EID: 84863766850     PISSN: None     EISSN: 21508097     Source Type: Conference Proceeding    
DOI: 10.14778/2078331.2078336     Document Type: Article

References (23)

1. I. Assent, A. Wenning, and T. Seidl. Approximation techniques for indexing the earth mover's distance in multimedia databases. In ICDE, page 11, April 2006.
2. I. Assent, M. Wichterich, T. Meisen, and T. Seidl. Effcient similarity search using the earth mover's distance for large multimedia databases. In ICDE, pages 307-316, 2008.
3. B. Brinkman and M. Charikar. On the impossibility of dimension reduction in l1. Journal of the ACM, 52(5):766-788, 2005.
4. S. D. Cohen and L. J. Guibas. The earth mover's distance: Lower bounds and invariance under translation. Technical report, 1997.
5. R. Fagin, A. Lotem, and M. Naor. Optimal aggregation algorithms for middleware. In ACM PODS, pages 102-113. ACM, 2001.
6. K. Grauman and T. Darrell. Fast contour matching using approximate earth mover's distance. IEEE CVPR, 1:220-227, June-2 July 2004.
7. F. S. Hillier and G. J. Lieberman. Introduction to Mathematical Programming. McGraw-Hill, 1990.
8. P. Hough. Method and means for recognizing complex patterns, Dec. 18 1962. US Patent 3,069,654.
9. E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra. Locally adaptive dimensionality reduction for indexing large time series databases. In ACM SIGMOD, pages 151{162, 2001.
10. H. Levy. Stochastic dominance and expected utility: survey and analysis. Management Science, pages 555-593, 1992.
11. V. Ljosa, A. Bhattacharya, and A. Singh. Indexing spatially sensitive distance measures using multi-resolution lower bounds. In EDBT, pages 865-883. Springer, 2006.
12. V. Ljosa and A. Singh. Apla: Indexing arbitrary probability distributions. In ICDE, pages 946-955. IEEE, 2007.
13. A. Oppenheim, R. Schafer, and J. Buck. Discrete-Time Signal Processing. Prentice Hall, 1999.
14. S. Rachev and L. Ruschendorf. Mass Transportation Problems: Theory. Springer series in statistics: Probability and its applications. Springer, 1998.
15. Y. Rubner, C. Tomasi, and L. J. Guibas. The earth mover's distance as a metric for image retrieval. Int. J. Comput. Vision, 40(2):99-121, 2000.
16. H. Samet. Foundations of multidimensional and metric data structures. Morgan Kaufmann, 2006.
17. D. Sinha and H. Zhou. Statistical timing analysis with coupling., IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 25(12):2965-2975, 2006.
18. S. Srinivasan. Local earth mover's distance and face warping. In IEEE International Conference on Multimedia and Expo, volume 2, pages 1227-1230 Vol.2, June 2004.
19. H. Tan and C. Ngo. Localized matching using Earth Mover's Distance towards discovery of common patterns from small image samples. Image and Vision Computing, 27(10):1470-1483, 2009.
20. M. Wichterich, I. Assent, P. Kranen, and T. Seidl. Effcient emd-based similarity search in multimedia databases via exible dimensionality reduction. In ACM SIGMOD, pages 199-212, 2008.
21. J. Xu, Z. Zhang, A. Tung, and G. Yu. Effcient and effective similarity search over probabilistic data based on earth mover's distance. Proceedings of the VLDB Endowment, 3(1-2):758{769, 2010. [22] B. Yi and C. Faloutsos. Fast time sequence indexing for arbitrary lp norms. In VLDB, pages 385{394. Citeseer, 2000.
22. B. Yi and C. Faloutsos. Fast time sequence indexing for arbitrary lp norms. In VLDB, pages 385-394. Citeseer, 2000.
23. D. Zhou, J. Li, and H. Zha. A new Mallows distance based metric for comparing clusterings. In ICML, pages 1028-1035. ACM, 2005.