Random Indexing Algorithm: Difference between revisions

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A [[Random Indexing Algorithm]] is a [[dimension compression algorithm]] that ...
A [[Random Indexing Algorithm]] is a [[dimension compression algorithm]] that ...
** …
** …
* <B>Counter-Exam
* <B>Counter-Example(s):</B>
ple(s):</B>
** [[Latent Semantic Analysis]].
** [[Latent Semantic Analysis]].
* <B>See:</B> [[Reflective Random Indexing]], [[Distributional Semantics]], [[Vector Space Model]], [[Random Projection]], [[Locality-Sensitive Hashing]], [[Sparse Distributed Memory]], [[Bit Vector]], [[Hamming Distance]], [[Document Clustering]].
* <B>See:</B> [[Reflective Random Indexing]], [[Distributional Semantics]], [[Vector Space Model]], [[Random Projection]], [[Locality-Sensitive Hashing]], [[Sparse Distributed Memory]], [[Bit Vector]], [[Hamming Distance]], [[Document Clustering]].

Latest revision as of 06:54, 7 January 2023

A Random Indexing Algorithm is a dimension compression algorithm that ...



References

2015

  1. QasemiZadeh, B. & Handschuh, S. (2014) Random Manhattan Indexing, In: Proceedings of the 25th International Workshop on Database and Expert Systems Applications.
  2. Johnson, W. and Lindenstrauss, J. (1984) Extensions of Lipschitz mappings into a Hilbert space, in Contemporary Mathematics. American Mathematical Society, vol. 26, pp. 189–206.
  3. Geva, S. & De Vries, C.M. (2011) TopSig: Topology Preserving Document Signatures, In: Proceedings of Conference on Information and Knowledge Management 2011, 24-28 October 2011, Glasgow, Scotland.

2010