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.
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References
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.
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.
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.
S. Dumais, Improving the retrieval of infomation from external sources, Behavior Research Methods, Instruments, & Computers, 23 (1991), pp. 229–236.
W. B. Frakes and R. Baeza-Yates, Information Retrieval: Data Structures and Algorithms, Prentice Hall, Englewood Cliffs, New Jersey, 1992.
G. H. Golub and C. F. Van Loan, Matrix Computations, Johns Hopkins Press, 2nd ed., 1989.
D. P. O’leary and S. Peleg, Digital image compression by outer product expansion, IEEE Transactions on Communications, 31 (1983), pp. 441–444.
G. Salton and M. J. Mcgill, Introduction to Modern Information Retrieval, McGraw-Hill, 1983.
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© 1999 Springer Science+Business Media New York
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Kolda, T.G., O’leary, D.P. (1999). Latent Semantic Indexing Via a Semi-Discrete Matrix Decomposition. In: Cybenko, G., O’Leary, D.P., Rissanen, J. (eds) The Mathematics of Information Coding, Extraction and Distribution. The IMA Volumes in Mathematics and its Applications, vol 107. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1524-0_5
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DOI: https://doi.org/10.1007/978-1-4612-1524-0_5
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