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Penalized Matrix Decomposition and Its Application in Co-word Analysis
Received date: 2015-05-18
Revised date: 2015-06-05
Online published: 2015-07-05
[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.
Shao Zuoyun , Li Xiuxia . Penalized Matrix Decomposition and Its Application in Co-word Analysis[J]. Library and Information Service, 2015 , 59(13) : 126 -133,148 . DOI: 10.13266/j.issn.0252-3116.2015.13.018
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