| 惩罚性矩阵分解及其在共词分析中的应用 |
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| 引用本文： | 邵作运,李秀霞.惩罚性矩阵分解及其在共词分析中的应用[J].图书情报工作,2015,59(13):126. |
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| 作者姓名： | 邵作运 李秀霞 |
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| 作者单位： | 1. 曲阜师范大学日照校区图书馆 日照 276826;
2. 曲阜师范大学传媒学院 日照 276826 |
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| 摘 要： | 目的/意义] 基于高维矩阵稀疏降维的思想,提出一种利用惩罚性矩阵分解(Penalized Matrix Decomposition,PMD)实现共词分析的新方法。方法/过程] 以"学科服务"为研究主题,根据PMD算法原理,在Matlab环境下分别实现特征词的提取、特征词的软聚类以及聚类效果的可视化。结果/结论] 与传统的共词分析方法对比,PMD算法在共词分析中具有独特的优势:提取的特征词比较全面,聚类数目便于确定,聚类结果易于理解。
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| 关 键 词： | PMD算法 共词分析 特征词提取 特征词软聚类 可视化 |
| 收稿时间： | 2015-05-18 |
Penalized Matrix Decomposition and Its Application in Co-word Analysis |
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| Shao Zuoyun,Li Xiuxia.Penalized Matrix Decomposition and Its Application in Co-word Analysis[J].Library and Information Service,2015,59(13):126. |
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| Authors: | Shao Zuoyun Li Xiuxia |
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| Institution: | 1. Library of Rizhao Campus, Qufu Normal University, Rizhao 276826;
2. School of Communication, Qufu Normal University, Rizhao 276826 |
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| Abstract: | Purpose/significance] Based on the idea of sparse dimension reduction, this paper proposes a new co-word analysis method with PMD (Penalized Matrix Decomposition).Method/process] According to the PMD algorithm principle, this paper takes the subject service as research theme, and separately extracts the feature words extracting, makes the feature words soft clustering and visualizes clustering results in the Matlab environment. Result/conclusion] Comparing with the traditional co-word analysis method, this paper finds that the PMD algorithm has some unique advantages in the co-word analysis, it can extract characteristic words more comprehensively, easily determine the clustering number, and get the more well clustering results. |
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| Keywords: | PMD algorithm co-word analysis feature words extraction feature words soft clustering visualization |
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