EDLSI with PSVD Updating

نویسندگان

  • April Kontostathis
  • Erin Moulding
  • Raymond J. Spiteri
چکیده

This paper describes the results obtained from the merging of two techniques that provide improvements to search and retrieval using Latent Semantic Indexing (LSI): Essential Dimensions of LSI (EDLSI) and partial singular value decomposition (PSVD) updating. EDLSI utilizes an implementation of LSI that requires the use of only a few dimensions in the LSI space. The PSVD updating and folding-up algorithms can be used to incrementally maintain the integrity of the LSI space as new content is added. In this paper, we show that EDLSI works as expected with these updating techniques, maintaining retrieval performance and dramatically improving runtime performance. Folding-in is another technique for incorporating documents into an LSI space; however, it does not deliberately maintain the orthogonality of the document and term vectors, and thus retrieval performance usually suffers. Interestingly we find that combining EDLSI with folding-in results in retrieval performance that is very similar to that of standard LSI but at a dramatically reduced cost.

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تاریخ انتشار 2010