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A density-based method for adaptive LDA model selection

文献类型：期刊论文

作者Cao, Juan1,2; Xia, Tian1,2; Li, Jintao1; Zhang, Yongdong1; Tang, Sheng1
刊名NEUROCOMPUTING
出版日期2009-03-01
卷号72期号:7-9页码:1775-1781
关键词Latent Dirichlet allocation Topic model Topic
ISSN号0925-2312
DOI10.1016/j.neucom.2008.06.011
英文摘要Topic models have been successfully used in information classification and retrieval. These models can capture word correlations in a collection of textual documents with a low-dimensional set of multinomial distribution, called "topics". However, it is important but difficult to select the appropriate number of topics for a specific dataset. In this paper, we study the inherent connection between the best topic structure and the distances among topics in Latent Dirichlet allocation (LDA), and propose a method of adaptively selecting the best LDA model based on density. Experiments show that the proposed method can achieve performance matching the best of LDA without manually tuning the number of topics. (C) 2008 Elsevier B.V. All rights reserved.
资助项目National Basic Research Program of China[2007CB311100] ; National High Technology and Research Development Program of China[2007AA01Z416] ; National Nature Science Foundation of China[60873165] ; Beijing New Star Project on Science Technology[200713071]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000264993200041
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.204/handle/2XEOYT63/11778]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Cao, Juan
作者单位1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100080, Peoples R China
2.Chinese Acad Sci, Grad Univ, Beijing 100039, Peoples R China
推荐引用方式
GB/T 7714		 Cao, Juan,Xia, Tian,Li, Jintao,et al. A density-based method for adaptive LDA model selection[J]. NEUROCOMPUTING,2009,72(7-9):1775-1781.
APA Cao, Juan,Xia, Tian,Li, Jintao,Zhang, Yongdong,&Tang, Sheng.(2009).A density-based method for adaptive LDA model selection.NEUROCOMPUTING,72(7-9),1775-1781.
MLA Cao, Juan,et al."A density-based method for adaptive LDA model selection".NEUROCOMPUTING 72.7-9(2009):1775-1781.

入库方式： OAI收割

来源：计算技术研究所

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