Dynamic Threshold Model Based Probabilistic Latent Semantic Analysis
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.
Yiming Wang Yangdong Ye Zhenfeng Zhu
School of Information Engineering Zhengzhou University,Zhengzhou,China
国际会议
厦门
英文
433-438
2014-08-19(万方平台首次上网日期,不代表论文的发表时间)