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Self-organizing weighted incremental probabilistic latent semantic analysis

文献类型：期刊论文

作者Li, Ning2,3; Shi, Zhongzhi2; He, Qing2; Zhuang, Fuzhen2; Yang, Kun1; Luo, Wenjuan2
刊名INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
出版日期2018-12-01
卷号9期号:12页码:1987-1998
关键词Probabilistic latent semantic analysis Weighted incremental learning Similarity Big data
ISSN号1868-8071
DOI10.1007/s13042-017-0681-9
英文摘要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.
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
语种英语
WOS记录号WOS:000450175600003
出版者SPRINGER HEIDELBERG
源URL[http://119.78.100.204/handle/2XEOYT63/4339]  
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.Natl Inst Metrol, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China
推荐引用方式
GB/T 7714		 Li, Ning,Shi, Zhongzhi,He, Qing,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,Shi, Zhongzhi,He, Qing,Zhuang, Fuzhen,Yang, Kun,&Luo, Wenjuan.(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.

入库方式： OAI收割

来源：计算技术研究所

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