IR
Supervised latent Dirichlet allocation with a mixture of sparse softmax
Li, Xiaoxu1,2; Ma, Zhanyu1; Peng, Pai3; Guo, Xiaowei3; Huang, Feiyue3; Wang, Xiaojie4; Guo, Jun1
2018-10-27
发表期刊NEUROCOMPUTING
ISSN0925-2312
卷号312页码:324-335
摘要Real data often show that from appearance within-class similarity is relatively low and between-class similarity is relatively high, which could increase the difficulty of classification. To classify this kind of data effectively, we learn multiple classification criteria simultaneously, and make different classification criterion be applied to classify different data for the purpose of relieving difficulty of fitting this kind of data and class label only by using a single classifier. Considering that topic model can learn high-level semantic features of the original data, and that mixture of softmax model is an efficient and effective probabilistic ensemble classification method, we embed a mixture of softmax model into latent Dirichlet allocation model, and propose a supervised topic model, supervised latent Dirichlet allocation with a mixture of softmax, and its improved version, supervised latent Dirichlet allocation with a mixture of sparse softmax. Next, we give their parameter estimation algorithms based on variational Expectation Maximization (EM) method. Moreover, we give an approximation method to classify unseen data, and analyze the convergence of the parameter estimation algorithm. Finally, we demonstrate the effectiveness of the proposed models by comparing them with some recently proposed approaches on two real image datasets and one text dataset. The experimental results demonstrate the good performance of the proposed models. (C) 2018 Published by Elsevier B.V.
关键词Supervised topic model Ensemble classification Mixture of softmax model Latent Dirichlet allocation
DOI10.1016/j.neucom.2018.05.077
收录类别SCI ; SCIE
语种英语
资助项目Beijing Nova Program[Z171100001117049]
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000438668100028
出版者ELSEVIER SCIENCE BV
EI入藏号20182505342825
EI主题词Classification (of information)
EI分类号716.1 Information Theory and Signal Processing - 921 Mathematics - 922.2 Mathematical Statistics
引用统计
被引频次:17[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://ir.lut.edu.cn/handle/2XXMBERH/32384
专题兰州理工大学
通讯作者Li, Xiaoxu
作者单位1.Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China;
2.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Gansu, Peoples R China;
3.Tecent Technol Shanghai Co Ltd, YoutuLab, Shanghai 200233, Peoples R China;
4.Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China
第一作者单位兰州理工大学
通讯作者单位兰州理工大学
推荐引用方式
GB/T 7714
Li, Xiaoxu,Ma, Zhanyu,Peng, Pai,et al. Supervised latent Dirichlet allocation with a mixture of sparse softmax[J]. NEUROCOMPUTING,2018,312:324-335.
APA Li, Xiaoxu.,Ma, Zhanyu.,Peng, Pai.,Guo, Xiaowei.,Huang, Feiyue.,...&Guo, Jun.(2018).Supervised latent Dirichlet allocation with a mixture of sparse softmax.NEUROCOMPUTING,312,324-335.
MLA Li, Xiaoxu,et al."Supervised latent Dirichlet allocation with a mixture of sparse softmax".NEUROCOMPUTING 312(2018):324-335.
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