Matrices with Low-Rank-Plus-Shift Structure: Partial SVD and Latent Semantic Indexing
- 1 January 2000
- journal article
- Published by Society for Industrial & Applied Mathematics (SIAM) in SIAM Journal on Matrix Analysis and Applications
- Vol. 21 (2) , 522-536
- https://doi.org/10.1137/s0895479898344443
Abstract
We 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.Keywords
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