Supervised latent Dirichlet allocation with a mixture of sparse softmax

doi:10.1016/j.neucom.2018.05.077

CORC > 兰州理工大学 > 兰州理工大学

	Supervised latent Dirichlet allocation with a mixture of sparse softmax
	Li, Xiaoxu 1,2; Ma, Zhanyu 1; Peng, Pai 3; Guo, Xiaowei 3; Huang, Feiyue 3; Wang, Xiaojie 4; Guo, Jun 1
刊名	NEUROCOMPUTING
	2018-10-27
卷号	312 页码:324-335
关键词	Supervised topic model Ensemble classification Mixture of softmax model Latent Dirichlet allocation
ISSN号	0925-2312
DOI	10.1016/j.neucom.2018.05.077
英文摘要	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.
资助项目	Beijing Nova Program[Z171100001117049]
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000438668100028
状态	已发表
内容类型	期刊论文
源URL	[http://119.78.100.223/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
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.