TOPIC SPLITTING： A HIERARCHICAL TOPIC MODEL BASED ON NON-NEGATIVE MATRIX FACTORIZATION

作     者：Rui Liu Xingguang Wang Deqing Wang Yuan Zuo He Zhang Xianzhu Zheng 

作者机构：SKLSDE School of Computer Science Beihang University Beijing 100191 China Department of Scientific Research Academy of Armored Force Engineering Beijing 100072 China 

出 版 物：《Journal of Systems Science and Systems Engineering》 (系统科学与系统工程学报（英文版）)

年 卷 期：2018年第27卷第4期

页      面：479-496页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 07[理学] 070104[理学-应用数学] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：国家自然科学基金 supported by the National Natural Science and Technology Basic Platform Projects 

主　　题：Hierarchical topic model non-negative matrix factorization hierarchical NMF topic splitting 

摘      要：Hierarchical topic model has been widely applied in many real applications, because it can build a hierarchy on topics with guaranteeing of topics＇ quality. Most of traditional methods build a hierarchy by adopting low-level topics as new features to construct high-level ones, which will often cause semantic confusion between low-level topics and high-level ones. To address the above problem, we propose a novel topic model named hierarchical sparse NMF with orthogonal constraint （HSOC）, which is based on non-negative matrix factorization and builds topic hierarchy via splitting super-topics into sub-topics. In HSOC, we introduce global independence, local independence and information consistency to constraint the split topics. Extensive experimental results on real-world corpora show that the purposed model achieves comparable performance on topic quality and better performance on semantic feature representation of documents compared with baseline methods.