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Self-organizing weighted incremental probabilistic latent semantic analysis
Li, Ning1,2; Luo, Wenjuan2; Yang, Kun3; Zhuang, Fuzhen2; He, Qing2; Shi, Zhongzhi2
2018-12-01
发表期刊INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
ISSN1868-8071
卷号9期号:12页码:1987-1998
摘要PLSA (Probabilistic Latent Semantic Analysis) is a popular topic modeling technique which has been widely applied to text mining applications to discover the underlying topics embedded in the data corpus. However, due to the variability of increasing data, it is necessary to discover the dynamic topics and process the large dataset incrementally. Moreover, PLSA models suffer from the problem of inferencing new documents. To overcome these problems, in this paper, we propose a novel Weighted Incremental PLSA algorithm called WIPLSA to dynamically discover topics and incrementally learn the topics from new documents. The experiments verify that the proposed WIPLSA could capture the dynamic topics hidden in the dynamic updating data corpus. Compared with PLSA, MAP PLSA and QB PLSA, WIPLSA performs better in perspexity on large dataset, which make it applicable for big data mining. In addition, WIPLSA has good performance in the application of document categorization.
关键词Probabilistic latent semantic analysis Weighted incremental learning Similarity Big data
DOI10.1007/s13042-017-0681-9
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[91546122] ; National Natural Science Foundation of China[61602438] ; National Natural Science Foundation of China[61573335] ; National Natural Science Foundation of China[61473273] ; National Natural Science Foundation of China[61473274] ; National Natural Science Foundation of China[61363058] ; National High-tech R&D Program of China (863 Program)[2014AA015105] ; National Science and Technology Support Program[2014BAK02B07] ; National major R&D program of Beijing Municipal Science & Technology Commission[Z161100002616032] ; Guangdong provincial science and technology plan projects[2015 B 010109005]
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000450175600003
出版者SPRINGER HEIDELBERG
引用统计
被引频次:13[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://119.78.100.204/handle/2XEOYT63/4340
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Ning
作者单位1.Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
3.Natl Inst Metrol, Beijing 100029, Peoples R China
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GB/T 7714
Li, Ning,Luo, Wenjuan,Yang, Kun,et al. Self-organizing weighted incremental probabilistic latent semantic analysis[J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,2018,9(12):1987-1998.
APA Li, Ning,Luo, Wenjuan,Yang, Kun,Zhuang, Fuzhen,He, Qing,&Shi, Zhongzhi.(2018).Self-organizing weighted incremental probabilistic latent semantic analysis.INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,9(12),1987-1998.
MLA Li, Ning,et al."Self-organizing weighted incremental probabilistic latent semantic analysis".INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 9.12(2018):1987-1998.
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