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.
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Fierro, R.D., Berry, M.W. (2002). Efficient Computation of the Riemannian SVD in Total Least Squares Problems in Information Retrieval. In: Van Huffel, S., Lemmerling, P. (eds) Total Least Squares and Errors-in-Variables Modeling. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-3552-0_31
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DOI: https://doi.org/10.1007/978-94-017-3552-0_31
Publisher Name: Springer, Dordrecht
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