On matrices with low-rank-plus-shift structure: Partial SVD and latent semantic indexing
The authors 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. The application they 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.
- Research Organization:
- Lawrence Berkeley National Lab., National Energy Research Scientific Computing Div., Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Research, Washington, DC (United States)
- DOE Contract Number:
- AC03-76SF00098
- OSTI ID:
- 663268
- Report Number(s):
- LBNL--42279; ON: DE98059390
- Country of Publication:
- United States
- Language:
- English
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