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SIAM. J. Matrix Anal. & Appl. 21, pp. 522-536 (15 pages)
Matrices with Low-Rank-Plus-Shift Structure: Partial SVD and Latent Semantic Indexing
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Add to FacebookWe present a detailed analysis of matrices satisfying the so-called low-rank-plus-shift property in connection with the computation of their partial singular value decomposition (SVD). The application we have in mind is latent semantic indexing for information retrieval, where the term-document matrices generated from a text corpus approximately satisfy this property. The analysis is motivated by developing more efficient methods for computing and updating partial SVD of large term-document matrices and gaining deeper understanding of the behavior of the methods in the presence of noise.
© 1999 Society for Industrial and Applied Mathematics
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