Smoothed Linear Modeling for Smooth Spectral Data

D. Hawkins, Edgard M. Maboudou-Tchao
{"title":"Smoothed Linear Modeling for Smooth Spectral Data","authors":"D. Hawkins, Edgard M. Maboudou-Tchao","doi":"10.1155/2013/604548","DOIUrl":null,"url":null,"abstract":"Classification and prediction problems using spectral data lead to high-dimensional data sets. Spectral data are, however, different from most other high-dimensional data sets in that information usually varies smoothly with wavelength, suggesting that fitted models should also vary smoothly with wavelength. Functional data analysis, widely used in the analysis of spectral data, meets this objective by changing perspective from the raw spectra to approximations using smooth basis functions. This paper explores linear regression and linear discriminant analysis fitted directly to the spectral data, imposing penalties on the values and roughness of the fitted coefficients, and shows by example that this can lead to better fits than existing standard methodologies.","PeriodicalId":14329,"journal":{"name":"International Journal of Spectroscopy","volume":"108 1","pages":"108-115"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Spectroscopy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2013/604548","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

Abstract

Classification and prediction problems using spectral data lead to high-dimensional data sets. Spectral data are, however, different from most other high-dimensional data sets in that information usually varies smoothly with wavelength, suggesting that fitted models should also vary smoothly with wavelength. Functional data analysis, widely used in the analysis of spectral data, meets this objective by changing perspective from the raw spectra to approximations using smooth basis functions. This paper explores linear regression and linear discriminant analysis fitted directly to the spectral data, imposing penalties on the values and roughness of the fitted coefficients, and shows by example that this can lead to better fits than existing standard methodologies.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
平滑光谱数据的平滑线性建模
使用光谱数据的分类和预测问题导致高维数据集。然而,光谱数据不同于大多数其他高维数据集,因为信息通常随波长平滑变化,这表明拟合模型也应随波长平滑变化。泛函数据分析广泛应用于光谱数据分析,通过将原始光谱的视角转变为使用光滑基函数的近似来实现这一目标。本文探讨了直接拟合光谱数据的线性回归和线性判别分析,对拟合系数的值和粗糙度施加惩罚,并通过实例表明,这可以比现有的标准方法更好地拟合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Synthesis and Characterization of Cobalt(III) and Copper(II) Complexes of 2-((E)-(6-Fluorobenzo[d]thiazol-2-ylimino) methyl)-4-chlorophenol: DNA Binding and Nuclease Studies—SOD and Antimicrobial Activities Application of Electrochemical Techniques on Study of Effect of Nano-ZnO in Conductive Polyaniline Containing Zinc-Rich Primer New Ni-Anthracene Complex for Selective and Sensitive Detection of 2,4,6-Trinitrophenol Application of Autofluorescence for Analysis of Medicinal Plants Fourier Spectroscopy: A Bayesian Way
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1