Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/27707
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dc.contributor.authorConnor, Richarden_UK
dc.contributor.authorCardillo, Franco Albertoen_UK
dc.contributor.authorMoss, Roberten_UK
dc.contributor.authorRabitti, Faustoen_UK
dc.contributor.editorBrisaboa, Nen_UK
dc.contributor.editorPedreira, Oen_UK
dc.contributor.editorZezula, Pen_UK
dc.date.accessioned2018-09-05T11:45:54Z-
dc.date.available2018-09-05T11:45:54Z-
dc.date.issued2013-12-31en_UK
dc.identifier.urihttp://hdl.handle.net/1893/27707-
dc.description.abstractJensen-Shannon divergence is a symmetrised, smoothed version of Küllback-Leibler. It has been shown to be the square of a proper distance metric, and has other properties which make it an excellent choice for many high-dimensional spaces in ℝ*. The metric as defined is however expensive to evaluate. In sparse spaces over many dimensions the Intrinsic Dimensionality of the metric space is typically very high, making similarity-based indexing ineffectual. Exhaustive searching over large data collections may be infeasible. Using a property that allows the distance to be evaluated from only those dimensions which are non-zero in both arguments, and through the identification of a threshold function, we show that the cost of the function can be dramatically reduced.en_UK
dc.language.isoenen_UK
dc.publisherSpringer Verlagen_UK
dc.relationConnor R, Cardillo FA, Moss R & Rabitti F (2013) Evaluation of Jensen-Shannon distance over sparse data. In: Brisaboa N, Pedreira O & Zezula P (eds.) Similarity Search and Applications: 6th International Conference, SISAP 2013, A Coruña, Spain, October 2-4, 2013, Proceedings. Lecture Notes in Computer Science, 8199. Similarity Search and Applications: 6th International Conference, SISAP 2013, Coruna, Spain, 02.10.2013-04.10.2013. Berlin, Heidelberg: Springer Verlag, pp. 163-168. https://doi.org/10.1007/978-3-642-41062-8_16en_UK
dc.relation.ispartofseriesLecture Notes in Computer Science, 8199en_UK
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectSparse dataen_UK
dc.subjectinverted indexen_UK
dc.subjectsparse vectoren_UK
dc.subjectintrinsic dimensionalityen_UK
dc.subjectlarge data collectionen_UK
dc.titleEvaluation of Jensen-Shannon distance over sparse dataen_UK
dc.typeConference Paperen_UK
dc.rights.embargodate2999-12-31en_UK
dc.rights.embargoreason[Connor et al 2013.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.identifier.doi10.1007/978-3-642-41062-8_16en_UK
dc.citation.jtitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_UK
dc.citation.issn0302-9743en_UK
dc.citation.spage163en_UK
dc.citation.epage168en_UK
dc.citation.publicationstatusPublisheden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emailrichard.connor@stir.ac.uken_UK
dc.citation.btitleSimilarity Search and Applications: 6th International Conference, SISAP 2013, A Coruña, Spain, October 2-4, 2013, Proceedingsen_UK
dc.citation.conferencedates2013-10-02 - 2013-10-04en_UK
dc.citation.conferencelocationCoruna, Spainen_UK
dc.citation.conferencenameSimilarity Search and Applications: 6th International Conference, SISAP 2013en_UK
dc.citation.isbn978-3-642-41061-1en_UK
dc.publisher.addressBerlin, Heidelbergen_UK
dc.contributor.affiliationUniversity of Strathclydeen_UK
dc.contributor.affiliationVisual Computing Group, CNR-ISTIen_UK
dc.contributor.affiliationUniversity of Strathclydeen_UK
dc.contributor.affiliationVisual Computing Group, CNR-ISTIen_UK
dc.identifier.isi000338111900016en_UK
dc.identifier.scopusid2-s2.0-84886443337en_UK
dc.identifier.wtid956098en_UK
dc.contributor.orcid0000-0003-4734-8103en_UK
dc.date.firstcompliantdepositdate2018-08-16en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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