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  2019, Vol. 32 Issue (8): 736-745    DOI: 10.16451/j.cnki.issn1003-6059.201908007
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Efficient Learning Algorithm for Maximum Entropy Discrimination Topic Models
CHEN Jianfei1, ZHU Jun1
1.Department of Computer Science and Technology, Tsinghua

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Abstract  

Time complexity of the existing supervised topic model training algorithms is generally linear to the number of topics and therefore their large-scale application is limited. To solve this problem, an efficient learning algorithm for maximum entropy discrimination of latent Dirichlet allocation(MedLDA) supervised subject model is proposed in this paper. The proposed algorithm is based on coordinate descent, and the number of iterations of training classifiers is less than that of the existing Monte Carlo algorithm for MedLDA. The algorithm also makes use of rejection sampling and efficient preprocessing technique to reduce the time complexity of training from linear to sub-linear with respect to the number of topics. The comparison experiments on multiple text corpora show that the proposed algorithm makes a great improvement in training speed compared with the existing Monte Carlo algorithm.

Key wordsSupervised Topic Models      Coordinate Descent      Gibbs Sampling      Rejection Sampling     
Received: 12 May 2019     
ZTFLH: TP 181  
Fund:

Supported by National Natural Science Foundation of China(No.61620106010), Beijing Natural Science Foundation(No.L172037)

Corresponding Authors: CHEN Jianfei(Corresponding author), Ph.D. His research interests include large-scale machine learning, probabilistic infe-rence and topic models.ZHU Jun(Corresponding author), Ph.D., professor. His research interest includes machine learning.   
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CHEN Jianfei,ZHU Jun. Efficient Learning Algorithm for Maximum Entropy Discrimination Topic Models[J]. , 2019, 32(8): 736-745.
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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201908007      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I8/736
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