MapReduce Based Personalized Locality Sensitive Hashing for Similarity Joins on Large Scale Data
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-04-30
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Locality Sensitive Hashing (LSH) has been proposed as an efficient techniquefor similarity joins for high dimensional data.
The efficiency and approximationrate of LSH depend on the number of generated false positive instances and falsenegative instances.
In many domains, reducing the number of false positives iscrucial.
Furthermore, in some application scenarios, balancing false positives andfalse negatives is favored.
To address these problems, in this paper we proposePersonalized Locality Sensitive Hashing (PLSH), where a new banding scheme isembedded to tailor the number of false positives, false negatives, and the sum ofboth.
PLSH is implemented in parallel using MapReduce framework to deal withsimilarity joins on large scale data.
Experimental studies on real and simulated dataverify the efficiency and effectiveness of our proposed PLSH technique, comparedwith state-of-the-art methods.
American Psychological Association (APA)
Wang, Jingjing& Lin, Chen. 2015. MapReduce Based Personalized Locality Sensitive Hashing for Similarity Joins on Large Scale Data. Computational Intelligence and Neuroscience،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1057674
Modern Language Association (MLA)
Wang, Jingjing& Lin, Chen. MapReduce Based Personalized Locality Sensitive Hashing for Similarity Joins on Large Scale Data. Computational Intelligence and Neuroscience No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1057674
American Medical Association (AMA)
Wang, Jingjing& Lin, Chen. MapReduce Based Personalized Locality Sensitive Hashing for Similarity Joins on Large Scale Data. Computational Intelligence and Neuroscience. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1057674
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057674