传感器融合
保险丝(电气)
计算机科学
样品(材料)
融合
数据挖掘
钥匙(锁)
点(几何)
数据类型
数据科学
人工智能
工程类
数学
语言学
色谱法
电气工程
计算机安全
哲学
化学
几何学
程序设计语言
作者
Silvana M. Azcarate,Rocío Ríos‐Reina,José Manuel Amigo,Héctor C. Goicoechea
标识
DOI:10.1016/j.trac.2021.116355
摘要
The use of data fusion methodologies has increased at the same rhythm as the capability of modern analytical laboratories of measuring sample from multiple sources. Almost all data fusion strategies can be grouped into three levels, they fuse the data differently with the sole aim of obtaining a better response (qualitative or quantitative) than that obtained by the instruments individually. One of the major key points for the data fusion methodologies to succeed is the understanding of the data structure obtained from a particular instrument. This point is not exhaustively commented in the literature focused on data fusion, sometimes paying too much attention to the algorithms instead. This manuscript explains data fusion from the structure of the different data obtained by different analytical platforms. Special attention will be given to the nature of the data and the relationships between the samples and the variables, as well as within the variables.
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