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An Overview of Latent Semantic Indexing
By odie | April 29, 2008
An Overview of Latent Semantic Indexing
Latent semantic indexing is a technic that projects queries and documents into room with latent semantic dimensions.
In the latent semantic space, a question and a particularize are comparable even if they do not share any of the identical terms if their terms are semantically similar.
Latent semantic indexing is identically metric to facts overlay foundation. Latent semantic indexing has fewer dimensions than the primary space and is a methodology for dimensionality reduction.
This reduction takes a set of objects that exist in a high-dimensional space and rearranges them and represents them in a lower dimensional space instead.
They are often represented in 2 or 3-dimensional space just for the purpose of visualization. Latent Semantic Indexing, or Latent semantic indexing is a mathematical application technique sometimes known as singular value decomposition.
The projection into the Latent semantic indexing space is selected so that the representations in the space of origin are changed as little as possible. Then it is measured by the sum of the squares of the difference.
There are numerous contrasting mappings for latent semantic indexing from high dimensional to low dimensional spaces.
Latent semantic indexing selects the optimal mapping in a faculty that minimizes the interval. Selection the numeral of dimensions is a unique difficulty.
A reduction is able to remove much of the noise while keeping too few dimensions may lose important information. Latent semantic indexing discharge is improved exceptionally after 10 to 12 dimensions and peaks at seventy to one hundred
dimensions.
Then it slowly begins to diminish again. There is a pattern of performance that is observed with other datasets as well.
Topics: LSI |
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