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- Random_indexing abstract "Random indexing is a dimension reduction method and computational framework for Distributional semantics, based on the insight that very-high-dimensional Vector Space Model implementations are impractical, that models need not grow in dimensionality when new items (e.g. new terminology) is encountered, and that a high-dimensional model can be projected into a space of lower dimensionality without compromising L2 distance metrics if the resulting dimensions are chosen appropriately, which is the original point of the random projection approach to dimension reduction first formulated as the Johnson–Lindenstrauss lemma. Locality-sensitive hashing has some of the same starting points. Random indexing, as used in representation of language, originates from the work of Pentti Kanerva on Sparse distributed memory, and can be described as an incremental formulation of a random projection.It can be also verified that random indexing is a random projection technique for the construction of Euclidean spaces---i.e. L2 normed vector spaces. In Euclidean spaces, random projections are elucidated using the Johnson–Lindenstrauss lemma.TopSig extends the Random Indexing model to produce bit vectors for comparison with the Hamming distance similarity function. It is used for improving the performance of information retrieval and document clustering. In a similar line of research, Random Manhattan Integer Indexing is proposed for improving the performance of the methods that employ the Manhattan distance between text units.".
- Random_indexing wikiPageID "37697003".
- Random_indexing wikiPageLength "3982".
- Random_indexing wikiPageOutDegree "15".
- Random_indexing wikiPageRevisionID "683519025".
- Random_indexing wikiPageWikiLink Bit_array.
- Random_indexing wikiPageWikiLink Bit_vector.
- Random_indexing wikiPageWikiLink Category:Dimension_reduction.
- Random_indexing wikiPageWikiLink Category:Machine_learning.
- Random_indexing wikiPageWikiLink Dimension_reduction.
- Random_indexing wikiPageWikiLink Dimensionality_reduction.
- Random_indexing wikiPageWikiLink Distributional_semantics.
- Random_indexing wikiPageWikiLink Document_clustering.
- Random_indexing wikiPageWikiLink Hamming_distance.
- Random_indexing wikiPageWikiLink Information_retrieval.
- Random_indexing wikiPageWikiLink Johnson–Lindenstrauss_lemma.
- Random_indexing wikiPageWikiLink Locality-sensitive_hashing.
- Random_indexing wikiPageWikiLink Manhattan_distance.
- Random_indexing wikiPageWikiLink Pentti_Kanerva.
- Random_indexing wikiPageWikiLink Roger_W._Schvaneveldt.
- Random_indexing wikiPageWikiLink Sparse_distributed_memory.
- Random_indexing wikiPageWikiLink Taxicab_geometry.
- Random_indexing wikiPageWikiLink Vector_Space_Model.
- Random_indexing wikiPageWikiLink Vector_space_model.
- Random_indexing wikiPageWikiLinkText "Random Indexing".
- Random_indexing wikiPageWikiLinkText "Random indexing".
- Random_indexing hasPhotoCollection Random_indexing.
- Random_indexing wikiPageUsesTemplate Template:Compsci-stub.
- Random_indexing wikiPageUsesTemplate Template:Reflist.
- Random_indexing subject Category:Dimension_reduction.
- Random_indexing subject Category:Machine_learning.
- Random_indexing hypernym Method.
- Random_indexing type Software.
- Random_indexing comment "Random indexing is a dimension reduction method and computational framework for Distributional semantics, based on the insight that very-high-dimensional Vector Space Model implementations are impractical, that models need not grow in dimensionality when new items (e.g.".
- Random_indexing label "Random indexing".
- Random_indexing sameAs m.0n_fbph.
- Random_indexing sameAs Q7291973.
- Random_indexing sameAs Q7291973.
- Random_indexing wasDerivedFrom Random_indexing?oldid=683519025.
- Random_indexing isPrimaryTopicOf Random_indexing.