Outlier Detection in Near Infra-Red Spectra with Self-Organizing Map

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.

作 者： Li Xiaoxia LI Gang LIN Ling Liu Yuliang WANG Yan LI Jian DU Jiang   作者單位： Li Xiaoxia(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)

LI Gang,LIN Ling,Liu Yuliang,WANG Yan(School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China)

LI Jian(School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300191, China)

DU Jiang(School of Electrical and Automatic Engineering, Hebei University of Technology, Tianjin 300130, China) 

刊 名： 天津大學(xué)學(xué)報（英文版）  EI 英文刊名： TRANSACTIONS OF TIANJIN UNIVERSITY  年，卷(期)： 2005 11(2)  分類號： P2  關(guān)鍵詞： outlier   near infra-red spectra   minimum subsets   resampling   self-organizing map  

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