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Parameter Optimization Method for Gaussian Mixture Model with Data Evolution

ISSN号：1000-0852
期刊名称：《水文》
时间：0
分类：TP391[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
作者机构：[1]College of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, China, [2]Information Technology Research Base of Civil Aviation Administration of China, Civil Aviation University of China, Tianjin, 300300, China
相关基金：Supported by the National Natural Science Foundation of China（61202137）; the Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China（CAAC-ITRB-201302）; the University Natural Science Basic Research Project of Jiangsu Province（13KJB520004）; the Fundamental Research Funds for the Central Universities（NS2012134）

中文摘要：

To learn from evolutionary experimental data points effectively,an evolutionary Gaussian mixture model based on constraint consistency(EGMM)is proposed and the corresponding method of parameter optimization is presented.Here,the Gaussian mixture model(GMM)is adopted to describe the data points,and the differences between the posterior probabilities of pairwise points under the current parameters are introduced to measure the temporal smoothness.Then,parameter optimization of EGMM can be realized by evolutionary clustering.Compared with most of the existing data analysis methods by evolutionary clustering,both the whole features and individual differences of data points are considered in the clustering framework of EGMM.It decreases the algorithm sensitivity to noises and increases the robustness of evaluated parameters.Experimental result shows that the clustering sequence really reflects the shift of data distribution,and the proposed algorithm can provide better clustering quality and temporal smoothness.

英文摘要：

To learn from evolutionary experimental data points effectively,an evolutionary Gaussian mixture model based on constraint consistency（EGMM）is proposed and the corresponding method of parameter optimization is presented.Here,the Gaussian mixture model（GMM）is adopted to describe the data points,and the differences between the posterior probabilities of pairwise points under the current parameters are introduced to measure the temporal smoothness.Then,parameter optimization of EGMM can be realized by evolutionary clustering.Compared with most of the existing data analysis methods by evolutionary clustering,both the whole features and individual differences of data points are considered in the clustering framework of EGMM.It decreases the algorithm sensitivity to noises and increases the robustness of evaluated parameters.Experimental result shows that the clustering sequence really reflects the shift of data distribution,and the proposed algorithm can provide better clustering quality and temporal smoothness.

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