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dc.contributor.authorQasemiZadeh, Behrang
dc.contributor.editorVaclav Matousek et al.
dc.identifier.citationQasemiZadeh, Behrang; (2015) Random Indexing Explained with High Probability . In: Vaclav Matousek et al eds. 18th International Conference, TSD 2015 PLZEŇ, CZECH REPUBLIC,en_US
dc.description.abstractRandom indexing (RI) is an incremental method for constructing a vector space model (VSM) with a reduced dimensionality. Previously, the method has been justified using the mathematical framework of Kanerva's sparse distributed memory. This justification, although intuitively plausible, fails to provide the information that is required to set the parameters of the method. In order to suggest criteria for the method's parameters, the RI method is revisited and described using the principles of linear algebra and sparse random projections in Euclidean spaces. These simple mathematics are then employed to suggest criteria for setting the method's parameters and to explain their influence on the estimated distances in the RI-constructed VSMs. The empirical results observed in an evaluation are reported to support the suggested guidelines in the paper.en_US
dc.relation.ispartof18th International Conference, TSD 2015en
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.subjectRandom indexingen_US
dc.subjectDimensionality reductionen_US
dc.subjectNatural language processingen_US
dc.subjectText analyticsen_US
dc.subjectVector space modelsen_US
dc.subjectRandom projectionsen_US
dc.titleRandom Indexing Explained with High Probabilityen_US
dc.typeConference Paperen_US
dc.contributor.funder|~|Science Foundation Ireland (SFI)|~|
dc.local.contactBehrang Qasemizadeh, Deri, Ida Business Park, Lower Dangan, Nui Galway. Email:

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland