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Random indexing revisited

QasemiZadeh, Behrang
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Identifiers
http://hdl.handle.net/10379/7050
 https://doi.org/10.13025/21183
Publication Date
2015-05-17
Keywords
Random Indexing, Dimensionality Reduction Techniques, Random Projections, Vector Space Models, Text Mining
Type
Conference Paper
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Citation
Qasemizadeh, Behrang (2015). Random indexing revisited. Paper presented at the 20th International Conference on Applications of Natural Language to Information Systems, NLDB, Passau, Germany.
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.
Funder
Science Foundation Ireland
Publisher
Springer
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Attribution-NonCommercial-NoDerivs 3.0 Ireland
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Data Science Institute (Conference Papers)