Latent Semantic Indexing Via a Semi-Discrete Matrix Decomposition

  • Tamara G. Kolda
  • Dianne P. O’leary
Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 107)

Abstract

With the electronic storage of documents comes the possibility of building search engines that can automatically choose documents relevant to a given set of topics. In information retrieval, we wish to match queries with relevant documents. Documents can be represented by the terms that appear within them, but literal matching of terms does not necessarily retrieve all relevant documents. There are a number of information retrieval systems based on inexact matches. Latent Semantic Indexing represents documents by approximations and tends to cluster documents on similar topics even if their term profiles are somewhat different. This approximate representation is usually accomplished using a low-rank singular value decomposition (SVD) approximation. In this paper, we use an alternate decomposition, the semi-discrete decomposition (SDD). For equal query times, the SDD does as well as the SVD and uses less than one-tenth the storage for the MEDLINE test set.

Key words

Information Retrieval Latent Semantic Indexing Singular Value Decomposition Semi-Discrete Decomposition References 

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Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Tamara G. Kolda
    • 1
  • Dianne P. O’leary
    • 2
  1. 1.Applied Mathematics ProgramUniversity of MarylandCollege ParkUSA
  2. 2.Department of Computer Science and Institute for Advanced Computer StudiesUniversity of MarylandCollege ParkUSA

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