Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure
Joint Authors
Zhang, Wen
Xiao, Fan
Li, Bin
Zhang, Siguang
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-07
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Recently, LSI (Latent Semantic Indexing) based on SVD (Singular Value Decomposition) is proposed to overcome the problems of polysemy and homonym in traditional lexical matching.
However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality.
In this paper, SVD on clusters is proposed to improve the discriminative power of LSI.
The contribution of this paper is three manifolds.
Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods.
Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters.
Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters.
Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods.
Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods.
American Psychological Association (APA)
Zhang, Wen& Xiao, Fan& Li, Bin& Zhang, Siguang. 2016. Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099575
Modern Language Association (MLA)
Zhang, Wen…[et al.]. Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1099575
American Medical Association (AMA)
Zhang, Wen& Xiao, Fan& Li, Bin& Zhang, Siguang. Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099575
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099575