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dc.contributor.authorQasemiZadeh, Behrang
dc.contributor.authorHandschuh, Siegfried
dc.date.accessioned2014-06-17T12:44:30Z
dc.date.available2014-06-17T12:44:30Z
dc.date.issued2014
dc.identifier.citationBehrang QasemiZadeh and Siegfried Handschuh (2014) Random Manhattan Indexing 25th International Workshop on Database and Expert Systems Applicationsen_US
dc.identifier.urihttp://hdl.handle.net/10379/4389
dc.descriptionConference paperen_US
dc.description.abstractVector space models (VSMs) are mathematically well-defined frameworks that have been widely used in text processing. In these models, high-dimensional, often sparse vectors represent text units. In an application, the similarity of vectors and hence the text units that they represent is computed by a distance formula. The high dimensionality of vectors, however, is a barrier to the performance of methods that employ VSMs. Consequently, a dimensionality reduction technique is employed to alleviate this problem. This paper introduces a new method, called Random Manhattan Indexing (RMI), for the construction of L1 normed VSMs at reduced dimensionality. RMI combines the construction of a VSM anddimension reduction into an incremental, and thus scalable, procedure. In order to attain its goal, RMI employs the sparse Cauchy random projections.en_US
dc.language.isoenen_US
dc.relation.ispartof25th International Workshop on Database and Expert Systems Applicationsen
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectvector space modelen_US
dc.subjectdimensionality reductionen_US
dc.subjectrandom projectionen_US
dc.subjectManhattan distanceen_US
dc.subjectretrieval modelsen_US
dc.titleRandom Manhattan Indexingen_US
dc.typeConference Paperen_US
dc.date.updated2014-06-17T10:55:31Z
dc.local.publishedsourcehttps://www.insight-centre.org/content/random-manhattan-indexing-randomized-scalable-method-semantic-similarity-measurement-l1en_US
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funder|~|SFI|~|
dc.internal.rssid6539006
dc.local.contactBehrang Qasemizadeh, Deri, Ida Business Park, Lower Dangan, Nui Galway. Email: behrang.qasemizadeh@deri.org
dc.local.copyrightcheckedYes
dc.local.versionACCEPTED
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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland