Which space partitioning tree to use for search?

Advances in Neural Information Processing Systems

Volumn , Issue , 2013, Pages

  Ram, P ^a   Gray, A G ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

VECTOR QUANTIZATION;

K-D TREE; LARGE MARGINS; NEAREST NEIGHBOR SEARCH; PRINCIPAL AXIS; RANDOM PROJECTION TREES; SEARCH PERFORMANCE; SPACE PARTITIONING TREES; TREE SEARCH;

BINARY TREES;

EID: 84899031888     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. J. H. Friedman, J. L. Bentley, and R. A. Finkel. An Algorithm for Finding Best Matches in Logarithmic Expected Time. ACM Transactions in Mathematical Software, 1977.
2. N. Verma, S. Kpotufe, and S. Dasgupta. Which Spatial Partition Trees are Adaptive to Intrinsic Dimension? In Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2009.
3. R.F. Sproull. Refinements to Nearest-Neighbor Searching in k-dimensional Trees. Algorithmica, 1991.
4. J. McNames. A Fast Nearest-Neighbor Algorithm based on a Principal Axis Search Tree. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001.
5. K. Fukunaga and P. M. Nagendra. A Branch-and-Bound Algorithm for Computing k-Nearest-Neighbors. IEEE Transactions on Computing, 1975.
6. D. Nister and H. Stewenius. Scalable Recognition with a Vocabulary Tree. In IEEE Conference on Computer Vision and Pattern Recognition, 2006.
7. S. Dasgupta and Y. Freund. Random Projection trees and Low Dimensional Manifolds. In Proceedings of ACM Symposium on Theory of Computing, 2008.
8. P. Ram, D. Lee, and A. G. Gray. Nearest-neighbor Search on a Time Budget via Max-Margin Trees. In SIAM International Conference on Data Mining, 2012.
9. L. Xu, J. Neufeld, B. Larson, and D. Schuurmans. Maximum Margin Clustering. Advances in Neural Information Processing Systems, 2005.
10. P. Indyk and R. Motwani. Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality. In Proceedings of ACM Symposium on Theory of Computing, 1998.
11. T. Liu, A. W. Moore, A. G. Gray, and K. Yang. An Investigation of Practical Approximate Nearest Neighbor Algorithms. Advances in Neural Information Proceedings Systems, 2005.
12. S. Dasgupta and K. Sinha. Randomized Partition Trees for Exact Nearest Neighbor Search. In Proceedings of the Conference on Learning Theory, 2013.
13. J. He, S. Kumar and S. F. Chang. On the Difficulty of Nearest Neighbor Search. In Proceedings of the International Conference on Machine Learning, 2012.
14. Y. Freund, S. Dasgupta, M. Kabra, and N. Verma. Learning the Structure of Manifolds using Random Projections. Advances in Neural Information Processing Systems, 2007.
15. D. R. Karger and M. Ruhl. Finding Nearest Neighbors in Growth-Restricted Metrics. In Proceedings of ACM Symposium on Theory of Computing, 2002.
16. B. Zhao, F. Wang, and C. Zhang. Efficient Maximum Margin Clustering via Cutting Plane Algorithm. In SIAM International Conference on Data Mining, 2008.
17. Y. Gong and S. Lazebnik. Iterative Quantization: A Procrustean Approach to Learning Binary Codes. In IEEE Conference on Computer Vision and Pattern Recognition, 2011.
18. R. Salakhutdinov and G. Hinton. Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure. In Artificial Intelligence and Statistics, 2007.
19. Y. Weiss, A. Torralba, and R. Fergus. Spectral Hashing. Advances of Neural Information Processing Systems, 2008.
20. J. Wang, S. Kumar, and S. Chang. Semi-Supervised Hashing for Scalable Image Retrieval. In IEEE Conference on Computer Vision and Pattern Recognition, 2010.
21. M. Norouzi and D. J. Fleet. Minimal Loss Hashing for Compact Binary Codes. In Proceedings of the International Conference on Machine Learning, 2011.
22. S. Lloyd. Least Squares Quantization in PCM. IEEE Transactions on Information Theory, 28(2):129-137, 1982.