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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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