robust estimation , computer vision , linear error in variable (EIV) model , multiple-structural data , mean-shift , C-step.  ," />
Journal of Systems Engineering and Electronics 2010, 21(5) 900-906 DOI:   10.3969/j.issn.1004-4132.2010.05.026  ISSN: 1004-4132 CN: 11-3018/N

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Email this article to a colleague robust estimation | computer vision | linear error in variable (EIV) model | multiple-structural data | mean-shift | C-step.  

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Authors
PubMed

Robust estimation algorithm for multiple-structural data

Zhiling Wang1 and Zonghai Chen1,2,*

1. Department of Automation, University of Science and Technology of China, Hefei 230027, P. R. China;
2. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, P. R. China

Abstract��

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.

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Received  Revised  Online:  
DOI: 10.3969/j.issn.1004-4132.2010.05.026
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