Dynamic Threshold Model Based Probabilistic Latent Semantic Analysis

摘要：

　　Probabilistic Latent Semantic Analysis(PLSA)is one of the main methods for texture analysis and computer vision.In practice,PLSA will result in overfitting problems,including the circumstance of unclear membership of topics and the case of high similarity between different topics.In this paper,we describe a dynamic threshold model based PLSA(dPLSA).It can make the ambiguous topic information more clear and objectified.Meanwhile,dPLSA can dynamically determine whether to merge the similar topics,in terms of the potential similarity between different topics.Experimental results on image data sets show that the proposed method outperforms its rival ones for solving the overfitting problems.

作者: Yiming Wang Yangdong Ye Zhenfeng Zhu

作者单位: School of Information Engineering Zhengzhou University,Zhengzhou,China

会议类型: 国际会议

会议名称: The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

会议地点: 厦门

会议语种:英文

页码: 433-438

在线出版日期: 2014-08-19（万方平台首次上网日期，不代表论文的发表时间）