Random Indexing Revisited

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9103)

Abstract

Random indexing is a method for constructing vector spaces at a reduced dimensionality. Previously, the method has been proposed using Kanerva’s sparse distributed memory model. Although intuitively plausible, this description fails to provide mathematical justification for setting the method’s parameters. The random indexing method is revisited using the principles of sparse random projections in Euclidean spaces in order to complement its previous delineation.

Keywords

Random indexing Dimensionality reduction techniques Vector space models Random projections 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.National University of IrelandGalway and University of PassauPassauGermany

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