Topic Selection in Latent Dirichlet Allocation

被引：0
|
作者：
Wang, Biao [1 ]
Liu, Zelong [2 ]
Li, Maozhen [2 ]
Liu, Yang [3 ]
Qi, Man [4 ]
机构：
[1] State Grid Sichuan Elect Power Res Inst, Chengdu, Peoples R China
[2] Brunel Univ, Sch Engn & Design, Uxbridge UB8 3PH, Middx, England
[3] Sichuan Univ, Sch Elect Engn & Informat Syst, Chengdu 610065, Peoples R China
[4] Canterbury Christ Church Univ, Dept Comp, Canterbury CT1 1QU, Kent, England
来源：
2014 11TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD) | 2014年
关键词：
MapReduce; job scheduling; data locality;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Latent Dirichlet Allocation (LDA) has been widely applied to text mining. LDA is a probabilistic topic model which processes documents as the probability distribution of topics. One challenging issue in application of LDA is to select the optimal number of topics in LDA model. This paper presents a topic selection method which considers the density of each topic and computes the most unstable topic structure through an iteration process. Evaluation results show that the proposed method can generate an optimal number of topics automatically with a small number of iterations.
引用
收藏
页码：756 / 760
页数：5
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