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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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[1] 李颖, 贾二鹏, 马力. 国内外共词分析研究综述[J]. 新世纪图书馆, 2012(1): 23-27. (Li Ying, Jia Erpeng, Ma Li. A Review of Domestic and International Co-word Analysis[J]. New Century Library, 2012(1): 23-27.)

[2] 张勤, 马费成. 国外知识管理研究范式——以共词分析为方法[J]. 管理科学学报, 2007, 10(6): 65-75. (Zhang Qin, Ma Feicheng. On Paradigm of Research Knowledge Manage- ment:A Bibliometric Analysis [J]. Journal of Management Sciences in China, 2007, 10(6): 65-75.)

[3] 陆宇杰, 张凤仙, 范并思. 基于共词分析的高校图书馆核心价值研究[J]. 大学图书馆学报, 2011, 29(6): 34-40. (Lu Yujie, Zhang Fengxian, Fan Bingsi. Research on the Core Value of Foreign Universities——Based on Co-word Analysis[J]. Journal of Academic Libraries, 2011, 29(6): 34-40.)

[4] Ding Y, Chowdhury G G, Foo S. Bibliometric Cartography of Information Retrieval Research by Using Co-word Analysis[J]. Information Processing & Management, 2001, 37(6): 817-842.

[5] Morris S A. Manifestation of Emerging Specialties in Journal Literature:A Growth Model of Papers, References, Exemplars, Bibliographic Coupling, Cocitation, and Clustering Coefficient Distribution[J]. Journal of the American Society for Information Science and Technology, 2005, 56(12): 1250-1273.

[6] 李纲, 李轶. 一种基于关键词加权的共词分析方法[J]. 情报科学, 2011, 29(3): 321-324. (Li Gang, Li Yi. An Approach to Co-word Analysis Based on Weighted Keywords[J]. Information Science, 2011, 29(3): 321-324.)

[7] 杨彦荣, 张阳. 加权共词分析法研究[J]. 情报理论与实践, 2011, 34(4): 61-63. (Yang Yanrong, Zhang Yang. Research on Weighted Co-word Analysis[J]. Information Studies:Theory & Application, 2011, 34(4): 61-63.)

[8] Witten D M, Tibshirani R, Hastie T. A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis[J]. Biostatistics, 2009, 10(3): 515-534.

[9] Zheng C H, Zhang L, Ng T Y, et al. Inferring the Transcriptional Modules Using Penalized Matrix Decomposition[C]. In:Proceedings of the 6th International Conference on Intelligent Computing, Changsha, China. 2010: 35-41.

[10] Zhang J, Zheng C H, Liu J X, et al. Discovering the Transcriptional Modules Using Microarray Data by Penalized Matrix Decomposition[J]. Computers in Biology and Medicine, 2011, 41(11): 1041-1050.

[11] Liu J X, Zheng C H, Xu Y. Extracting Plants Core Genes Responding to Abiotic Stresses by Penalized Matrix Decomposition[J]. Computers in Biology and Medicine, 2012, 42(5): 582-589.

[12] 王娟, 范少萍, 郑春厚. 基于惩罚性矩阵分解的文本聚类分析[J]. 情报学报, 2012, 31(9): 998-1008. (Wang Juan, Fan Shaoping, Zheng Chunhou. Analysis of Text Clustering Based on Penalized Matrix Decomposition[J]. Journal of the China Society for Scientific and Technical Information, 2012, 31(9): 998-1008.)

[13] 郭春侠, 叶继元. 基于共词分析的国外图书情报学研究热点[J]. 图书情报工作, 2011, 55(20): 19-22. (Guo Chunxia, Ye Jiyuan. Hot Topics of Library and Information Science Abroad Between 2005 and 2009 Based on Co-word Analysis Method[J] Library and Information Service, 2011, 55(20): 19-22.)

[14] Pearson K. On Lines and Planes of Closest Fit to Systems of Points in Space[J]. Philosophical Magazine, 1901, 2 (6): 559-572.

[15] Abdi H, Williams L J. Principal Component Analysis[J]. Wiley Interdisciplinary Reviews:Computational Statistics, 2010, 2(4):433-459.

[16] 孙晓宁, 储节旺. 近十年知识管理领域硕博士学位论文研究热点分析——以共词分析为方法[J]. 情报杂志, 2012, 31(6): 433-459. (Sun Xiaoning, Chu Jiewang. On Hotspots of Master and Ph. D. Degree's Dissertations in the Field of Knowledge Management During the Last Decade:A Co-word Analysis[J]. Journal of Intelligence, 2012, 31(6): 433-459.)

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