메뉴 건너뛰기




Volumn , Issue , 2014, Pages 1127-1138

DSH: Data Sensitive Hashing for high-dimensional k-NN search

Author keywords

Hashing; High Dimensions; LSH; Similarity Search

Indexed keywords

ORTHOGONAL FUNCTIONS; QUERY PROCESSING;

EID: 84904366650     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2588555.2588565     Document Type: Conference Paper
Times cited : (70)

References (23)
  • 1
    • 38749118638 scopus 로고    scopus 로고
    • Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
    • A. Andoni and P. Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In FOCS, pages 459-468, 2006.
    • (2006) FOCS , pp. 459-468
    • Andoni, A.1    Indyk, P.2
  • 3
    • 5044238246 scopus 로고    scopus 로고
    • Boostmap: A method for efficient approximate similarity rankings
    • V. Athitsos, J. Alon, S. Sclaroff, and G. Kollios. Boostmap: A method for efficient approximate similarity rankings. In CVPR, pages II-268, 2004.
    • (2004) CVPR
    • Athitsos, V.1    Alon, J.2    Sclaroff, S.3    Kollios, G.4
  • 5
    • 52649111585 scopus 로고    scopus 로고
    • Nearest neighbor retrieval using distance-based hashing
    • V. Athitsos, M. Potamias, P. Papapetrou, and G. Kollios. Nearest neighbor retrieval using distance-based hashing. In ICDE, pages 327-336, 2008.
    • (2008) ICDE , pp. 327-336
    • Athitsos, V.1    Potamias, M.2    Papapetrou, P.3    Kollios, G.4
  • 6
    • 0042378381 scopus 로고    scopus 로고
    • Laplacian eigenmaps for dimensionality reduction and data representation
    • M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation, pages 1373-1396, 2003.
    • (2003) Neural Computation , pp. 1373-1396
    • Belkin, M.1    Niyogi, P.2
  • 8
    • 84983110889 scopus 로고
    • A desicion-theoretic generalization of on-line learning and an application to boosting
    • Y. Freund and R. E. Schapire. A desicion-theoretic generalization of on-line learning and an application to boosting. In Computational Learning Theory, pages 23-37, 1995.
    • (1995) Computational Learning Theory , pp. 23-37
    • Freund, Y.1    Schapire, R.E.2
  • 9
    • 84862662275 scopus 로고    scopus 로고
    • Locality-sensitive hashing scheme based on dynamic collision counting
    • J. Gan, J. Feng, Q. Fang, and W. Ng. Locality-sensitive hashing scheme based on dynamic collision counting. In SIGMOD, pages 541-552, 2012.
    • (2012) SIGMOD , pp. 541-552
    • Gan, J.1    Feng, J.2    Fang, Q.3    Ng, W.4
  • 10
    • 15044355327 scopus 로고    scopus 로고
    • Similarity search in high dimensions via hashing
    • A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. In VLDB, pages 518-529, 1999.
    • (1999) VLDB , pp. 518-529
    • Gionis, A.1    Indyk, P.2    Motwani, R.3
  • 11
    • 0041664272 scopus 로고    scopus 로고
    • Index-driven similarity search in metric spaces (survey article)
    • G. R. Hjaltason and H. Samet. Index-driven similarity search in metric spaces (survey article). TODS, pages 517-580, 2003.
    • (2003) TODS , pp. 517-580
    • Hjaltason, G.R.1    Samet, H.2
  • 12
    • 77953184849 scopus 로고    scopus 로고
    • Kernelized locality-sensitive hashing for scalable image search
    • B. Kulis and K. Grauman. Kernelized locality-sensitive hashing for scalable image search. In ICCV, pages 2130-2137, 2009.
    • (2009) ICCV , pp. 2130-2137
    • Kulis, B.1    Grauman, K.2
  • 13
    • 84955245129 scopus 로고    scopus 로고
    • Multi-probe lsh: Efficient indexing for high-dimensional similarity search
    • 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.
    • (2007) VLDB , pp. 950-961
    • Lv, Q.1    Josephson, W.2    Wang, Z.3    Charikar, M.4    Li, K.5
  • 14
    • 13444286179 scopus 로고    scopus 로고
    • Locality preserving projections
    • X. Niyogi. Locality preserving projections. In NIPS, pages 153-160, 2004.
    • (2004) NIPS , pp. 153-160
    • Niyogi, X.1
  • 15
    • 33244462877 scopus 로고    scopus 로고
    • Entropy based nearest neighbor search in high dimensions
    • R. Panigrahy. Entropy based nearest neighbor search in high dimensions. In SODA, pages 1186-1195, 2006.
    • (2006) SODA , pp. 1186-1195
    • Panigrahy, R.1
  • 16
    • 80455158726 scopus 로고    scopus 로고
    • Learning a nonlinear embedding by preserving class neighbourhood structure
    • R. Salakhutdinov and G. E. Hinton. Learning a nonlinear embedding by preserving class neighbourhood structure. In AI and Statistics, pages 412-419, 2007.
    • (2007) AI and Statistics , pp. 412-419
    • Salakhutdinov, R.1    Hinton, G.E.2
  • 17
    • 84863752005 scopus 로고    scopus 로고
    • Bayesian locality sensitive hashing for fast similarity search
    • V. Satuluri and S. Parthasarathy. Bayesian locality sensitive hashing for fast similarity search. VLDB, pages 430-441, 2012.
    • (2012) VLDB , pp. 430-441
    • Satuluri, V.1    Parthasarathy, S.2
  • 18
    • 0037806811 scopus 로고    scopus 로고
    • The boosting approach to machine learning: An overview
    • R. E. Schapire. The boosting approach to machine learning: An overview. LECTURE NOTES IN STATISTICS, pages 149-172, 2003.
    • (2003) Lecture Notes in Statistics , pp. 149-172
    • Schapire, R.E.1
  • 19
    • 0345414554 scopus 로고    scopus 로고
    • Fast pose estimation with parameter-sensitive hashing
    • G. Shakhnarovich, P. Viola, and T. Darrell. Fast pose estimation with parameter-sensitive hashing. In ICCV, pages 750-757, 2003.
    • (2003) ICCV , pp. 750-757
    • Shakhnarovich, G.1    Viola, P.2    Darrell, T.3
  • 20
    • 70849088674 scopus 로고    scopus 로고
    • Quality and efficiency in high dimensional nearest neighbor search
    • Y. Tao, K. Yi, C. Sheng, and P. Kalnis. Quality and efficiency in high dimensional nearest neighbor search. In SIGMOD, pages 563-576, 2009.
    • (2009) SIGMOD , pp. 563-576
    • Tao, Y.1    Yi, K.2    Sheng, C.3    Kalnis, P.4
  • 21
    • 0012951952 scopus 로고    scopus 로고
    • A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces
    • R. Weber, H.-J. Schek, and S. Blott. A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In VLDB, pages 194-205, 1998.
    • (1998) VLDB , pp. 194-205
    • Weber, R.1    Schek, H.-J.2    Blott, S.3
  • 22
  • 23
    • 84944319598 scopus 로고    scopus 로고
    • Indexing the distance: An efficient method to knn processing
    • C. Yu, B. C. Ooi, K.-L. Tan, and H. V. Jagadish. Indexing the distance: An efficient method to knn processing. In VLDB, pages 421-430, 2001.
    • (2001) VLDB , pp. 421-430
    • Yu, C.1    Ooi, B.C.2    Tan, K.-L.3    Jagadish, H.V.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.