dc.contributor.author | TOUGAS, JANE E. | |
dc.date.accessioned | 2017-02-10T19:01:25Z | |
dc.date.available | 2017-02-10T19:01:25Z | |
dc.date.issued | 2006 | |
dc.identifier.uri | http://hdl.handle.net/10222/72685 | |
dc.description | 2005 NSIS Graduate Student Special Prize for High Merit | |
dc.description.abstract | 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. 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.
In a rapidly expanding environment, a term-document matrix is altered often as new
documents and terms are added. Recomputing the PSVD of the term-document matrix
each time these slight alterations occur can be prohibitively expensive.
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. The folding-in method is
computationally inexpensive, but may cause deterioration in the accuracy of the PSVD.
The PSVD-updating method is computationally more expensive than the folding-in
method, but better maintains the accuracy of the PSVD. Folding-up is a new method
that combines folding-in and PSVD-updating. Folding-up is faster than either recomputing
the PSVD or PSVD-updating, but avoids the degradation in the PSVD that can occur
when the folding-in method is used on its own. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Nova Scotian Institute of Science | en_US |
dc.relation.ispartof | Proceedings of the Nova Scotian Institute of Science | en_US |
dc.title | A COMPARISON OF METHODS FOR MODIFYING THE PARTIAL SINGULAR VALUE DECOMPOSITION IN LATENT SEMANTIC INDEXING | en_US |
dc.type | Text | en_US |
dc.identifier.volume | 43 | |
dc.identifier.issue | 2 | |
dc.identifier.startpage | 211 | |