数据采集与处理2016，Vol.31Issue(6)：1097-1105,9.
光谱数据的特征挖掘降维方法
Dimension Reduction of Spectral Data Based on Feature Mining
摘要
Abstract
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Key words
spectral data/feature mining/sequential forward selection/dimension reduction分类
数理科学引用本文复制引用
戴琼海,张晶,李菲菲,范静涛..光谱数据的特征挖掘降维方法[J].数据采集与处理,2016,31(6):1097-1105,9.基金项目
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。 （）
