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dimensions of latent semantic indexing

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									Dimensions Of Latent Semantic Indexing

Latent semantic indexing is commonly used to match web
search queries to documents in retrieval applications.
LSI has improved the retrieval applications.

It has improved retrieval performance for some, but
not all, collections when compared to traditional
vector space retrieval or VSR.

Latent semantic indexing allows a search engine to
determine what a page is about by searching for one or
more keywords that are selected by the user.

LSI adds an important step to the document index
process. Latent semantic indexing records keywords
that a document contains as well as examines the
document collection as a whole.

By placing importance on related words, or words in
similar positions, LSA has a net effect of making the
value of pages lower so they only match specific
terms.

Latent semantic indexing has fewer dimensions than the
original space and is a method 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 two or three-dimensional
space just for the purpose of visualization.

Latent Semantic Indexing is a mathematical application
technique sometimes known as singular value
decomposition. The number of dimensions needed is
typically large.

This has implications for indexing run time, query run
time and the amount of memory required. In order to
plot the position of the web page, you need to think
of the page in terms of a three-dimensional shape.

Using three words instead of three lines, you are able
to achieve this image. The position of every page that
contains these three words is known as a term space.

Each page forms a vector in the space and the vectors
direction and magnitude determine how many times the
three words appear in the structure.

								
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