Robust estimation algorithm for multiple-structural data  

作　　者：Zhiling Wang Zonghai Chen 

机构地区：[1]Department of Automation, University of Science and Technology of China, Hefei 230027, R R. China [2]National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, P. R. China

出　　处：《Journal of Systems Engineering and Electronics》2010年第5期900-906,共7页系统工程与电子技术（英文版）

基　　金：supported by the National High Technology Research and Development Program of China (863 Program) (2007AA04Z227)

摘　　要：This paper proposes a robust method of parameter estimation and data classification for multiple-structural data based on the linear error in variable（EIV） model.The traditional EIV model fitting problem is analyzed and a robust growing algorithm is developed to extract the underlying linear structure of the observed data.Under the structural density assumption,the C-step technique borrowed from the Rousseeuw＇s robust MCD estimator is used to keep the algorithm robust and the mean-shift algorithm is adopted to ensure a good initialization.To eliminate the model ambiguities of the multiple-structural data,statistical hypotheses tests are used to refine the data classification and improve the accuracy of the model parameter estimation.Experiments show that the efficiency and robustness of the proposed algorithm.This paper proposes a robust method of parameter estimation and data classification for multiple-structural data based on the linear error in variable（EIV） model.The traditional EIV model fitting problem is analyzed and a robust growing algorithm is developed to extract the underlying linear structure of the observed data.Under the structural density assumption,the C-step technique borrowed from the Rousseeuw＇s robust MCD estimator is used to keep the algorithm robust and the mean-shift algorithm is adopted to ensure a good initialization.To eliminate the model ambiguities of the multiple-structural data,statistical hypotheses tests are used to refine the data classification and improve the accuracy of the model parameter estimation.Experiments show that the efficiency and robustness of the proposed algorithm.

关 键 词：robust estimation computer vision linear error in variable（EIV） model multiple-structural data MEAN-SHIFT C-step. 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] TP311.13[自动化与计算机技术—计算机科学与技术]