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New Technology of Library and Information Service  2014, Vol. 30 Issue (3): 88-95    DOI: 10.11925/infotech.1003-3513.2014.03.13
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The Penalized Matrix Decomposition Method of Extracting Core Characteristic Words——Taking Co-word Analysis as an Example
Yu Xianzi1, Gao Yinglian2, Ma Chunxia1, Liu Jinxing1
1 Department of Information Technology and Communication, QuFu Normal University, Rizhao 276826, China;
2 Library of QuFu Normal University, Rizhao 276826, China
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[Objective] Highlight core characteristic words directly by reducing the high-dimensional co-matrix sparely in co-word analysis. [Methods] This article proposes, based on the Penalized Matrix Decomposition (PMD) method, a method to extract core characteristic words from texts of characteristic words.The authors experiment on articles which are related to university libraries that take advantage of SNS, and use Matlab R2012a to decompose high-dimensional co-word matrix by PMD. [Results] By using PMD method, 65 core characteristic words are extracted from all 1648 characteristic words, which more than 34 characteristic words that extracted by the principal components analysis, and also reveal research hotspots of the university libraries using social networks. [Limitations] The authors don't refer to all the characteristic words that acquired from literature, and have a certain subjectivity. [Conclusions] Converting into sparse matrix by PMD, core characteristic words are comprehended and explained more easily, meanwhile, they can show some marginal subjects.

Key wordsPMD      Extracting core characteristic words      PCA     
Received: 10 September 2013      Published: 15 April 2014
:  G250  

Cite this article:

Yu Xianzi, Gao Yinglian, Ma Chunxia, Liu Jinxing. The Penalized Matrix Decomposition Method of Extracting Core Characteristic Words——Taking Co-word Analysis as an Example. New Technology of Library and Information Service, 2014, 30(3): 88-95.

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