Self-organizing weighted incremental probabilistic latent semantic analysis

doi:10.1007/s13042-017-0681-9

	Self-organizing weighted incremental probabilistic latent semantic analysis
	Li, Ning 1,2; Luo, Wenjuan 2; Yang, Kun 3; Zhuang, Fuzhen 2; He, Qing 2; Shi, Zhongzhi 2
	2018-12-01
发表期刊	INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
ISSN	1868-8071
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
DOI	10.1007/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
推荐引用方式 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.