Introducing off-diagonal elements to singular value matrix in probabilistic 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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Dimension reduction methods, such as Latent Semantic Indexing (LSI), when applied to semantic space built upon text collections, improve information retrieval, information filtering and word sense disambiguation. A new dual probability model based on the similarity concepts is introduced to provide deeper understanding of LSI. Semantic associations can be quantitatively characterized by their s...
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We present results from using Random Indexing for Latent Semantic Analysis to handle Singular Value Decomposition tractability issues. We compare Latent Semantic Analysis, Random Indexing and Latent Semantic Analysis on Random Indexing reduced matrices. In this study we use a corpus comprising 1003 documents from the MEDLINE-corpus. Our results show that Latent Semantic Analysis on Random Index...
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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: Transactions of the Japanese Society for Artificial Intelligence
سال: 2011
ISSN: 1346-0714,1346-8030
DOI: 10.1527/tjsai.26.262