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)


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 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    M. W. Berry, S. T. Dumais, And G. W. O’brien, Using linear algebra for intelligent information retrieval, SIAM Review, 37 (1995), pp. 573–595.MathSciNetzbMATHCrossRefGoogle Scholar
  2. [2]
    J. P. Callan, B. Croft, and S. M. Harding, The INQUERY retrieval system, in Proceedings of the Third International Conference on Database and Expert Systems Applications, Springer-Verlag, 1992, pp. 78–83.Google Scholar
  3. [3]
    S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, And R. Harsh-Man, Indexing by latent semantic analysis, Journal of the Society for Information Science, 41 (1990), pp. 391–407.CrossRefGoogle Scholar
  4. [4]
    S. Dumais, Improving the retrieval of infomation from external sources, Behavior Research Methods, Instruments, & Computers, 23 (1991), pp. 229–236.CrossRefGoogle Scholar
  5. [5]
    W. B. Frakes and R. Baeza-Yates, Information Retrieval: Data Structures and Algorithms, Prentice Hall, Englewood Cliffs, New Jersey, 1992.Google Scholar
  6. [6]
    G. H. Golub and C. F. Van Loan, Matrix Computations, Johns Hopkins Press, 2nd ed., 1989.Google Scholar
  7. [7]
    D. P. O’leary and S. Peleg, Digital image compression by outer product expansion, IEEE Transactions on Communications, 31 (1983), pp. 441–444.CrossRefGoogle Scholar
  8. [8]
    G. Salton and M. J. Mcgill, Introduction to Modern Information Retrieval, McGraw-Hill, 1983.Google Scholar

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

Personalised recommendations