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集中式环境下的局部敏感哈希算法综述 被引量：1

Review on Locality Sensitive Hashing in Centralized Environment
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摘要 局部敏感哈希算法是一种很流行的高维相似性查找算法。通过总结多篇已发表论文,介绍了集中式环境下的局部敏感哈希算法及其实现,分析了各种局部敏感哈希算法的特点和优缺点。在近似最近邻查询中的广泛应用证实了局部敏感哈希算法的有效性。 Locality sensitive hashing is a very popular high dimensional similarity search algorithm. LSH algorithm and its implementation in centralized environment were introduced. Features, advantages and disadvantages of LSH algorithm were analyzed by summarizing several published papers. LSH algorithm was proved to be effective in widespread applications of approximate nearest neighbor query.
作者 刘根平
机构地区 宁波大学
出处 《移动通信》 2015年第10期46-51,共6页 Mobile Communications
基金 宁波市自然科学基金(2014A610023)
关键词 高维数据 相似性搜索 KNN查询 局部敏感哈希算法 high dimensional data similarity search KNN query locality sensitive hashing
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献31

  • 1W Jeon, Y M Cheng. Efficient speaker search over large populations using kernelized locality-sensitive hashing[C]. Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on. IEEE, 2012: 4261-4264.
  • 2M S Charikar. Similarity estimation techniques from rounding algorithms[C], in Proceedings of the thirty- fourth annual ACM symposium on theory of computing. ACM, 2002: 380-388.
  • 3W Dong, Z Wang, W Josephson, et al. Modeling LSH for performance tuning[C]. Proceedings of the 17th ACM conference on information and knowledge management. ACM, 2008: 669-678.
  • 4M Bawa, T Condie, P Ganesan. LSH forest: self-tuning indexes for similarity search[C]. Proceedings of the 14th international conference on World Wide Web. ACM, 2005:651-660.
  • 5S Paisitkriangkrai, T Mei, J Zhang, et al. Scalable clip- based near-duplicate video detection with ordinal measure[C]. Proceedings of the ACM International Conference on Image and Video Retrieval. ACM, 2010: 121-128.
  • 6G S Manku, A Jain, A Das Sarma. Detecting near- duplicates for web crawling[C]. Proceedings of the 16th international conference on World Wide Web. ACM 2007:141-150.
  • 7P Indyk, R Motwani. Approximate nearest neighbors: towards removing the curse of dimensionality[C]. Proceedings of the thirtieth annual ACM symposium on theory of computing. ACM, 1998:604-613.
  • 8A Gionis, P Indyk, R Motwani. Similarity search dimensions via hashing[J]. VLDB, 1999:518-529.
  • 9R Buaba, A Homaifar, M Gebril, et al. Satellite image retrieval using low memory locality sensitive hashing in Euclidean space[J]. Earth Science Informatics, 2011,4(1): 17-28.
  • 10汤春蕾,董家麒.基于LSH的时间子序列查询算法[J].计算机学报,2012,35(11):2228-2236. 被引量：6

二级参考文献20

  • 1Keogh E. Exact indexing of dynamic time warping//Proceed- ings of the VLDB. Hong Kong, China, 2002: 406-417.
  • 2Rafiei D, Mendelzon A O. Querying time series data based on similarity. IEEE Transactions on Knowledge and Data Engineering, 2000, 12(5): 675-693.
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  • 7Chen L, Ng R T. On the marriage of Lp-norms and edit dis- tance//Proceedings of the 30th International Conference on Very Large Data Bases. 2004:792-803.
  • 8Agrawal R, Faloutsos C, Swami A. Efficient similarity search in sequence databases//Proceedings of the FODO. Chicago, Illinois, USA, 1993:69-84.
  • 9Beckmann Net al. The R.tree: An efficient and robust access method for points and rectangles//Proceedings of the SIGMOD. Atlantic City, NJ, USA, 1990:322-331.
  • 10Keogh E et al. Locally adaptive dimensionality reduction for indexing large time series databases//Proceedings of the SIGMOD. Santa Barbara, CA, USA, 2001:151-162.

共引文献5

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二级引证文献6

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2015年 第10期

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