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dc.contributor.authorConnor, Richarden_UK
dc.contributor.authorMoss, Roberten_UK
dc.contributor.editorNavarro, Gonzaloen_UK
dc.contributor.editorPestov, Vladimiren_UK
dc.description.abstractWe investigate a distance metric, previously defined for the measurement of structured data, in the more general context of vector spaces. The metric has a basis in information theory and assesses the distance between two vectors in terms of their relative information content. The resulting metric gives an outcome based on the dimensional correlation, rather than magnitude, of the input vectors, in a manner similar to Cosine Distance. In this paper the metric is defined, and assessed, in comparison with Cosine Distance, for its major properties: semantics, properties for use within similarity search, and evaluation efficiency. We find that it is fairly well correlated with Cosine Distance in dense spaces, but its semantics are in some cases preferable. In a sparse space, it significantly outperforms Cosine Distance over TREC data and queries, the only large collection for which we have a human-ratified ground truth. This result is backed up by another experiment over movielens data. In dense Cartesian spaces it has better properties for use with similarity indices than either Cosine or Euclidean Distance. In its definitional form it is very expensive to evaluate for high-dimensional sparse vectors; to counter this, we show an algebraic rewrite which allows its evaluation to be performed more efficiently. Overall, when a multivariate correlation metric is required over positive vectors, SED seems to be a better choice than Cosine Distance in many circumstances.en_UK
dc.publisherSpringer Verlagen_UK
dc.relationConnor R & Moss R (2012) A multivariate correlation distance for vector spaces. In: Navarro G & Pestov V (eds.) Similarity Search and Applications: 5th International Conference, SISAP 2012, Toronto, ON, Canada, August 9-10, 2012. Proceedings. Lecture Notes in Computer Science, 7404. Similarity Search and Applications: 5th International Conference, SISAP 2012, Toronto, 09.08.2012-10.08.2012. Berlin, Heidelberg: Springer Verlag, pp. 209-225.
dc.relation.ispartofseriesLecture Notes in Computer Science, 7404en_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.subjectDistance metricen_UK
dc.subjectmultivariate correlationen_UK
dc.subjectvector spaceen_UK
dc.subjectcosine distanceen_UK
dc.subjectsimilarity searchen_UK
dc.titleA multivariate correlation distance for vector spacesen_UK
dc.typeConference Paperen_UK
dc.rights.embargoreason[Connor Moss 2012.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.citation.jtitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_UK
dc.type.statusVoR - Version of Recorden_UK
dc.citation.btitleSimilarity Search and Applications: 5th International Conference, SISAP 2012, Toronto, ON, Canada, August 9-10, 2012. Proceedingsen_UK
dc.citation.conferencedates2012-08-09 - 2012-08-10en_UK
dc.citation.conferencenameSimilarity Search and Applications: 5th International Conference, SISAP 2012en_UK
dc.publisher.addressBerlin, Heidelbergen_UK
dc.contributor.affiliationUniversity of Strathclydeen_UK
dc.contributor.affiliationUniversity of Strathclydeen_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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