Lanzhou University of Technology Institutional Repository (LUT_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 |
ISSN | 0925-2312 |
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 |
DOI | 10.1016/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 | |
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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