Application of EM Algorithm in Parameter Estimation of <i>p</i>-Norm Mixture Model

PENG Fei; WANG Zhong; MENG Qingxu; PAN Xiong; QIU Fengqin; YANG Yufeng

doi:10.13203/j.whugis20200172

Volume 47 Issue 9

Sep. 2022

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Geomatics and Information Science of Wuhan University > 2022 > 47(9): 1432-1438. > DOI: 10.13203/j.whugis20200172

PENG Fei, WANG Zhong, MENG Qingxu, PAN Xiong, QIU Fengqin, YANG Yufeng. Application of EM Algorithm in Parameter Estimation of p-Norm Mixture Model[J]. Geomatics and Information Science of Wuhan University, 2022, 47(9): 1432-1438. DOI: 10.13203/j.whugis20200172

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Application of EM Algorithm in Parameter Estimation of p-Norm Mixture Model

1.
College of Naval Architecture and Ocean Engineering, Naval University of Engineering, Wuhan 430033, China
2.
School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China
3.
School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China

Funds:

The National Natural Science Foundation of China 42174010

The National Natural Science Foundation of China 41874009

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Author Bio:
PENG Fei, PhD, associate professor, specializes in ship building technology and ship overall design research. E-mail: pengfei75@qq.com
Corresponding author:
WANG Zhong, PhD, lecturer. E-mail: wangzhonghj@sohu.com
Received Date: April 16, 2020
Available Online: September 19, 2022
Published Date: September 04, 2022

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Abstract

Abstract

Objectives Aiming at the mixed observation data of multiple distribution forms, a expectation-maximum (EM) combined p-norm distributed model(EM_p) is established.
Methods Considering that the mixed number in the mixture model belongs to incomplete data, the EM algorithm is introduced to estimate the parameters of the mixture model and the p-model mixture model parameters are derived in detail. The estimated iteration formula and the corresponding iteration steps are given.The mixture Gaussian distribution data, Laplace distribution and Gaussian distribution mixture data, and the residual data of measured global positioning system(GPS) observations are used to verify the correctness and adaptability of the formula in this paper.
Results and Conclusions The results of the calculation examples show that, compared with the single probability distribution, the p-norm mixture model can accurately reflect the actual situation of the data distribution, and the model parameters estimated by the EM algorithm have higher accuracy.
- mixture model,
- parameter estimation,
- p-norm distribution,
- expectation-maximum(EM) algorithm

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