光谱数据的特征挖掘降维方法  

Dimension Reduction of Spectral Data Based on Feature Mining

作　　者：戴琼海[1] 张晶[1] 李菲菲[1] 范静涛[1] 

机构地区：[1]清华大学自动化系,北京100084

出　　处：《数据采集与处理》2016年第6期1097-1105,共9页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金委国家重大科研仪器设备研制专项(61327902)资助项目;国家自然科学基金面上(61271450)资助项目

摘　　要："去繁存精"的光谱数据解耦方法可去除高维光谱数据的大量冗余,提炼其特征谱段,是光谱仪器得以广泛应用的重要基础。应用各异性和光谱特征优选方法普适性所构成的矛盾,在一定程度上制约了光谱仪器的应用。本文提出了序列前向选择(Sequential forward selection,SFS)的光谱特征自适应数据挖掘方法,生成最优变量组合作为支持向量机(Support vector machine,SVM)分类模型的输入,在对光谱数据降维的同时,实现了高精度的数据分类。本文方法可有效解决大量光谱数据的多类分类问题,并在红木分类中得到了实际验证和应用,为破解因光谱特征峰高度混叠而难以进行主观经验特征选择的困境提供了新思路。The method of spectral data analysis ,which can remove a lot of redundancy of high‐dimensional spectral data and extract its characteristic spectrum ,is an important foundation for the widespread appli‐cation of spectral instruments .The contradiction of the applicability of the heterogeneity and spectral characteristics of the method of universal selection ,to a certain extent ,restricts the application of spec‐tral instruments ,need to be resolved .In this paper ,a sequential forward selection （SFS） spectral feature adaptive data mining method is proposed to generate the optimal combination of variables as support vec‐tor machine （SVM ） classification model input ,to achieve the spectral data reduction and obtain a high‐precision data classification .This method can effectively solve the problem of multi‐class classification of a large number of spectral data ,which is proved and applied in the classification of mahogany .It provides a new way to solve the difficulty of subjective experience feature selection in height‐aliasing of spectral peaks .

关 键 词：光谱数据 特征挖掘 序列前向选择 数据降维 

分 类 号：O433.4[机械工程—光学工程]