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Computational Intelligence and Neuroscience
Volume 2016, Article ID 1096271, 11 pages
http://dx.doi.org/10.1155/2016/1096271
Research Article

Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure

1Center on Big Data Sciences, Beijing University of Chemical Technology, Beijing 100039, China
2Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China

Received 2 March 2016; Accepted 8 June 2016

Academic Editor: Toshihisa Tanaka

Copyright © 2016 Wen Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.