Use of t-distributed stochastic neighbour embedding in vibrational spectroscopy

IF 2.3 4区 化学 Q1 SOCIAL WORK Journal of Chemometrics Pub Date : 2024-03-23 DOI:10.1002/cem.3544
François Stevens, Beatriz Carrasco, Vincent Baeten, Juan A. Fernández Pierna
{"title":"Use of t-distributed stochastic neighbour embedding in vibrational spectroscopy","authors":"François Stevens,&nbsp;Beatriz Carrasco,&nbsp;Vincent Baeten,&nbsp;Juan A. Fernández Pierna","doi":"10.1002/cem.3544","DOIUrl":null,"url":null,"abstract":"<p>The <i>t-distributed stochastic neighbour embedding</i> algorithm or <i>t-SNE</i> is a non-linear dimension reduction method used to visualise multivariate data. It enables a high-dimensional dataset, such as a set of infrared spectra, to be represented on a single, typically two-dimensional graph, revealing its global and local structure. t-SNE is very popular in the machine learning community and has been applied in many fields, generally with the aim of visualising large datasets. In vibrational spectroscopy, t-SNE is gaining notoriety but principal component analysis (PCA) remains by far the reference method for exploratory analysis and dimension reduction. However, t-SNE may represent a real aid in the analysis of vibrational spectroscopic datasets. It provides an at-a-glance global view of the dataset allowing to distinguish the main factors influencing the spectral signal and the hierarchy between these factors, and gives an indication on the possibility of performing predictive modelling. It can also provide great support in the choice of the pre-processing, by comparing rapidly different general pre-processing approaches according to their effect on the variable of interest. Here we propose to illustrate these advantages using different datasets. We also propose an approach based on a synergy between the t-SNE and PCA methods, allowing respective advantages of each to be exploited.</p>","PeriodicalId":15274,"journal":{"name":"Journal of Chemometrics","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemometrics","FirstCategoryId":"92","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cem.3544","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOCIAL WORK","Score":null,"Total":0}
引用次数: 0

Abstract

The t-distributed stochastic neighbour embedding algorithm or t-SNE is a non-linear dimension reduction method used to visualise multivariate data. It enables a high-dimensional dataset, such as a set of infrared spectra, to be represented on a single, typically two-dimensional graph, revealing its global and local structure. t-SNE is very popular in the machine learning community and has been applied in many fields, generally with the aim of visualising large datasets. In vibrational spectroscopy, t-SNE is gaining notoriety but principal component analysis (PCA) remains by far the reference method for exploratory analysis and dimension reduction. However, t-SNE may represent a real aid in the analysis of vibrational spectroscopic datasets. It provides an at-a-glance global view of the dataset allowing to distinguish the main factors influencing the spectral signal and the hierarchy between these factors, and gives an indication on the possibility of performing predictive modelling. It can also provide great support in the choice of the pre-processing, by comparing rapidly different general pre-processing approaches according to their effect on the variable of interest. Here we propose to illustrate these advantages using different datasets. We also propose an approach based on a synergy between the t-SNE and PCA methods, allowing respective advantages of each to be exploited.

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
在振动光谱学中使用 t 分布随机邻域嵌入法
t-distributed stochastic neighbour embedding algorithm(t-SNE)是一种非线性降维方法,用于可视化多变量数据。它能将高维数据集(如一组红外光谱)表示在一个单一的、典型的二维图形上,从而揭示其全局和局部结构。t-SNE 在机器学习领域非常流行,并已应用于许多领域,其目的通常是将大型数据集可视化。在振动光谱学中,t-SNE 的名气越来越大,但到目前为止,主成分分析(PCA)仍是探索性分析和降维的参考方法。然而,t-SNE 可以真正帮助分析振动光谱数据集。它提供了一个一目了然的数据集全局视图,可以区分影响光谱信号的主要因素以及这些因素之间的层次关系,并提供了进行预测建模的可能性。通过快速比较不同的一般预处理方法对相关变量的影响,它还能为选择预处理方法提供极大的支持。在此,我们建议使用不同的数据集来说明这些优势。我们还提出了一种基于 t-SNE 和 PCA 方法之间协同作用的方法,从而可以利用这两种方法各自的优势。
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
Journal of Chemometrics
Journal of Chemometrics 化学-分析化学
CiteScore
5.20
自引率
8.30%
发文量
78
审稿时长
2 months
期刊介绍: The Journal of Chemometrics is devoted to the rapid publication of original scientific papers, reviews and short communications on fundamental and applied aspects of chemometrics. It also provides a forum for the exchange of information on meetings and other news relevant to the growing community of scientists who are interested in chemometrics and its applications. Short, critical review papers are a particularly important feature of the journal, in view of the multidisciplinary readership at which it is aimed.
期刊最新文献
Issue Information Issue Information Resampling as a Robust Measure of Model Complexity in PARAFAC Models Population Power Curves in ASCA With Permutation Testing A Non‐Linear Model for Multiple Alcohol Intakes and Optimal Designs Strategies
×
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