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| Outlier Detection in Near Infra-Red Spectra with Self-Organizing Map | |
| 引用本文： | 李晓霞 李刚 林凌 刘玉良 王焱 李健 杜江. Outlier Detection in Near Infra-Red Spectra with Self-Organizing Map[J]. 天津大学学报(英文版), 2005, 11(2): 129-132 |
| 作者姓名： | 李晓霞 李刚 林凌 刘玉良 王焱 李健 杜江 |
| 作者单位： | [1]SchoolofPrecisionInstrumentsandOpto-ElectronicsEngineering,TianjinUniversity,Tianjin300072,China／／SchoolofElectricalandAutomaticEngineering,HebeiUniversityofTechnology,Tianjin300130,China [2]SchoolofPrecisionInstrumentsandOpto-ElectronicsEngineering,TianjinUniversity,Tianjin300072,China [3]SchoolofMechanicalEngineering,TianjinUniversityofTechnology,Tianjin300191,China [4]SchoolofElectricalandAutomaticEngineering,HebeiUniversityofTechnology,Tianjin300130,China |
| 摘 要： | A new method to detect multiple outliers in multivariate data is proposed. It is a combination of minimum subsets, resampling and self-organizing map (SOM) algorithm introduced by Kohonen,which provides a robust way with neural network. In this method, the number and organization of the neurons are selected by the characteristics of the spectra, e. g., the spectra data are often changed linearly with the concentration of the components and are often measured repeatedly, etc. So the spatial distribution of the neurons can be arranged by this characteristic. With this method, all the outliers in the spectra can be detected, which cannot be solved by the traditional method, and the speed of computation is higher than that of the traditional neural network method. The results of the simulation and the experiment show that this method is simple, effective, intuitionistic and all the outliers in the spectra can be detected in a short time. It is useful when associated with the regression model in the near infra-red research. |
| 关 键 词： | 近红外光谱 代谢物 化学计量技术 临床医学 诊断技术 |
Outlier Detection in Near Infra-Red Spectra with Self-Organizing Map |
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| Li Xiaoxia,LI Gang,LIN Ling,Liu Yuliang,WANG Yan,LI Jian,DU Jiang. Outlier Detection in Near Infra-Red Spectra with Self-Organizing Map[J]. Transactions of Tianjin University, 2005, 11(2): 129-132 | |
| Authors: | Li Xiaoxia LI Gang LIN Ling Liu Yuliang WANG Yan LI Jian DU Jiang |
| Affiliation: | 1. School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China;School of Electrical and Automatic Engineering, Hebei University of Technology, Tianjin 300130, China 2. School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China 3. School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300191, China 4. School of Electrical and Automatic Engineering, Hebei University of Technology, Tianjin 300130, China |
| Abstract: | A new method to detect multiple outliers in multivariate data is proposed. It is a combination of minimum subsets, resampling and self-organizing map (SOM) algorithm introduced by Kohonen,which provides a robust way with neural network. In this method, the number and organization of the neurons are selected by the characteristics of the spectra, e.g., the spectra data are often changed linearly with the concentration of the components and are often measured repeatedly, etc. So the spatial distribution of the neurons can be arranged by this characteristic. With this method, all the outliers in the spectra can be detected, which cannot be solved by the traditional method, and the speed of computation is higher than that of the traditional neural network method. The results of the simulation and the experiment show that this method is simple, effective, intuitionistic and all the outliers in the spectra can be detected in a short time. It is useful when associated with the regression model in the near infra-red research. |
| Keywords: | outlier near infra-red spectra minimum subsets resampling self-organizing map |
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