Updating the partial singular value decomposition in latent semantic indexing
نویسندگان
چکیده
منابع مشابه
Updating the partial singular value decomposition in latent semantic indexing
Latent semantic indexing (LSI) is a method of information retrieval that relies heavily on the partial singular value decomposition (PSVD) of the term-document matrix representation of a dataset. Calculating the PSVD of large term-document matrices is computationally expensive; hence in the case where terms or documents are merely added to an existing dataset, it is extremely beneficial to upda...
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Latent Semantic Indexing (LSI) is an information retrieval (IR) method that connects IR with numerical linear algebra by representing a dataset as a term-document matrix. Because of the tremendous size of modern databases, such matrices can be very large. The partial singular value decomposition (PSVD) is a matrix factorization that captures the salient features of a matrix, while using much le...
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ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2007
ISSN: 0167-9473
DOI: 10.1016/j.csda.2006.12.018