DISCRIMINATIVE LDA

摘要：

This paper is aim to improve the discrimination capability of LDA model through unsupervised feature selection. Experimental results show that if the interference of general word and general topic can be removed, the discrimination capability of LDA model will be increased. The key problem is how to find supervised information to evaluate features. The LDA topics are assumed reasonable. Therefore, topics will offer surprised information for word features’ selection. Constraint coming from the surprised information is added to the LDA objective function. Finally, a heuristic algorithm is presented to obtain the solution. Experiments show that the Discriminative LDA can significantly improve the information gain of topics.

作者: Weiran Xu Mingzhi Dong YunHang Lin Jun Guo Guang Chen

作者单位: School of Information and Communication Engineering,Beijing University of Posts and Telecommunicatio School of Information and Communication Engineering, Beijing University of Posts and Telecommunicati

会议类型: 国际会议

会议名称: 2010 2nd IEEE International Conference on Network Infrasturcture & Digital Content(2010年第二届IEEE网络基础设施与数字内容国际会议 IC-NIDC 2010)

会议地点: 北京

会议语种:英文

页码: 287-292

在线出版日期: 2010-09-24（万方平台首次上网日期，不代表论文的发表时间）