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...
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