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	GSLDA: Supervised topic model with graph regularization
	Yan, Qiuling ; Yang, Dongqing
	2014
英文摘要	In this work, we study the problem of regularizing supervised topic model using graph structure. Supervised topic model generates each document independently, whereas in many applications there are links among documents, which are quite useful for refining topics. To overcome this limit of supervised topic model, we propose a regularization framework using graph structure. By leveraging both textual content and link structure, the output of the proposed model can promote effect of topic extraction and social network analysis simultaneously. Experiment results on two real datasets demonstrate the effectiveness of the proposed approach. ? 2014 IEEE.; EI; CPCI-S(ISTP); 0
语种	英语
DOI标识	10.1109/FSKD.2014.6980906
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/412895]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Yan, Qiuling,Yang, Dongqing. GSLDA: Supervised topic model with graph regularization. 2014-01-01.

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