Locality-sensitive hashing scheme based on dynamic collision counting

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2012, Pages 541-552

  Gan, Junhao ^a   Feng, Jianlin ^a   Fang, Qiong ^b   Ng, Wilfred ^b  

^a SUN YAT SEN UNIVERSITY   (China)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

dynamic collision counting; locality sensitive hashing

Indexed keywords

DATA OBJECTS; EXPERIMENTAL STUDIES; HASH TABLE; HIGH DIMENSIONAL SPACES; HIGH-DIMENSIONAL; LOCALITY SENSITIVE HASHING; ON DYNAMICS; QUERY OBJECT; REAL DATA SETS; SEARCH PROBLEM; STATE-OF-THE-ART METHODS; SYNTHETIC DATASETS; THEORETICAL STUDY;

HASH FUNCTIONS;

CONTENT BASED RETRIEVAL;

EID: 84862662275     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2213836.2213898     Document Type: Conference Paper

References (15)

1. J. L. Bentley. K-D trees for semi-dynamic point sets. In Symposium on Computational Geometry, 1990.
2. M. Datar, P. Indyk, N. Immorlica, and V. S. Mirrokni. Locality-sensitive hashing scheme based on p-stable distributions. In SoCG, pages 253-262, 2004.
3. R. Fagin, R. Kumar, and D. Sivakumar. Efficient similarity search and classiffication via rank aggregation. In SIGMOD, pages 301-312, 2003.
4. A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. In VLDB, pages 518-529, 1999.
5. R. Graham, D. Knuth, and O. Patashnik. Concrete Mathematics: A Foundation for Computer Science. 1994.
6. A. Guttman. R-trees: A dynamic index structure for spatial searching. In SIGMOD, pages 47-57, 1984.
7. W. Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association 58 (301), pages 13-30, 1963.
8. P. Indyk and R. Motwani. Approximate nearest neighbors: Towards removing the curse of dimensionality. In STOC, 1998.
9. H. V. Jagadish, B. C. Ooi, K.-L. Tan, C. Yu, and R. Zhang. idistance: An adaptive b+-tree based indexing method for nearest neighbor search. ACM TODS, pages 364-397, 2005.
10. J. M. Kleinberg. Two algorithms for nearest-neighbor search in high dimensions. In STOC, 1997.
11. Q. Lv, W. Josephson, Z. Wang, M. Charikar, and K. Li. Multi-probe LSH: Efficient indexing for high-dimensional similarity search. In VLDB, pages 950-961, 2007.
12. H. Samet. Foundations of Multidimensional and Metric Data Structures. 2006.
13. Y. Tao, K. Yi, C. Sheng, and P. Kalnis. Efficient and accurate nearest neighbor and closest pair search in high dimensional space. ACM TODS, 35, 2010.
14. J. Zhai, Y. Lou, and J. Gehrke. Atlas: A probabilistic algorithm for high dimensional similarity search. In SIGMOD, pages 997-1008, 2011.
15. D. Zhang, D. Agrawal, G. Chen, and A. K. H. Tung. Hashfile: An efficient index structure for multimedia data. In ICDE, pages 1103-1114, 2011.