Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/71159
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dc.titleNon-metric locality-sensitive hashing
dc.contributor.authorMu, Y.
dc.contributor.authorYan, S.
dc.date.accessioned2014-06-19T03:20:32Z
dc.date.available2014-06-19T03:20:32Z
dc.date.issued2010
dc.identifier.citationMu, Y.,Yan, S. (2010). Non-metric locality-sensitive hashing. Proceedings of the National Conference on Artificial Intelligence 1 : 539-544. ScholarBank@NUS Repository.
dc.identifier.isbn9781577354642
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/71159
dc.description.abstractNon-metric distances are often more reasonable compared with metric ones in terms of consistency with human perceptions. However, existing locality-sensitive hashing (LSH) algorithms can only support data which are gauged with metrics. In this paper we propose a novel locality-sensitive hashing algorithm targeting such non-metric data. Data in original feature space are embedded into an implicit reproducing kernel Kreǐn space and then hashed to obtain binary bits. Here we utilize the norm-keeping property of p-stable functions to ensure that two data's collision probability reflects their non-metric distance in original feature space. We investigate various concrete examples to validate the proposed algorithm. Extensive empirical evaluations well illustrate its effectiveness in terms of accuracy and retrieval speedup. Copyright © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.sourcetitleProceedings of the National Conference on Artificial Intelligence
dc.description.volume1
dc.description.page539-544
dc.description.codenPNAIE
dc.identifier.isiutNOT_IN_WOS
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