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 |
| DOI | 10.1016/j.neucom.2008.06.011 |
| 通讯作者 | Cao, juan(caojuan@ict.ac.cn) |
| 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. |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence |
| 语种 | 英语 |
| WOS记录号 | WOS:000264993200041 |
| 出版者 | ELSEVIER SCIENCE BV |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2401132 |
| 专题 | 中国科学院大学 |
| 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. |
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来源：中国科学院大学
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