Updating the partial singular value decomposition in latent semantic indexing Sex hookup chat rooms
Moreover, it is shown how the compu-tational cost can be reduced further, again without impacting performance, through a combination of updating and folding-in.
LSI uses a matrix factorization method known as the partial singular value decomposition (PSVD).
Calculating the PSVD of a large term-document matrix is computationally expensive.
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Folding-in is one method of adding new documents or terms to an LSI database; updating the PSVD of the existing LSI database is another.
We investigate the use of the PSVD updating methods proposed by Zha and Simon (1999, SIAM J. An algorithm for iteratively computing the PSVD of a matrix using the document updating method will then be presented.
The tremendous size of the Internet and modem databases has made efficient searching and information retrieval (IR) important.
Latent semantic indexing (LSI) is an IR method that represents a dataset as a term-document matrix.
The application they have in mind is Latent Semantic Indexing for information retrieval where the term-document matrices generated from a text corpus approximately satisfy this property.
The analysis is motivated by developing more efficient methods for computing and updating partial SVD of large term-document matrices and gaining deeper understanding of the behavior of the methods in the presence of noise.