Efficient Computation of the Riemannian SVD in Total Least Squares Problems in Information Retrieval

Ricardo D. Fierro () and Michael W. Berry ()
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Ricardo D. Fierro: California State University, Department of Mathematics
Michael W. Berry: University of Tennessee, Department of Computer Science

A chapter in Total Least Squares and Errors-in-Variables Modeling, 2002, pp 353-364 from Springer

Abstract: Abstract Recently, a nonlinear generalization of the singular value decomposition (SVD), called the Riemannian-SVD (R-SVD), for solving full rank total least squares problems was extended to low rank matrices within the context of latent semantic indexing (LSI) in information retrieval. This new approach, called RSVD-LSI, is based on the full SVD of an m × n term-by-document matrix A and requires the dense m × m left singular matrix U and the n × n right singular matrix V. Here, m corresponds to the size of the dictionary and n corresponds to the number of documents. We dicuss this method along with an efficient implementation of the method that takes into account the sparsity of A.

Keywords: information retrieval; Riemannian singular value decomposition. (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-94-017-3552-0_31

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DOI: 10.1007/978-94-017-3552-0_31

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