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The Representation of Chemical Spectral Data for Classification

机译:化学光谱数据的分类表示

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The classification of unknown samples is among the most common problems found in chemometrics. For this purpose, a proper representation of the data is very important. Nowadays, chemical spectral data are analyzed as vectors of discretized data where the variables have not connection, and other aspects of their functional nature e.g. shape differences (structural), are also ignored. In this paper, we study some advanced representations for chemical spectral datasets, and for that we make a comparison of the classification results of 4 datasets by using their traditional representation and two other: Functional Data Analysis and Dissimilarity Representation. These approaches allow taking into account the information that is missing in the traditional representation, thus better classification results can be achieved. Some suggestions are made about the more suitable dissimilarity measures to use for chemical spectral data.
机译:未知样品的分类是化学计量学中最常见的问题之一。为此,正确表示数据非常重要。如今,化学光谱数据已被分析为离散数据的矢量,其中变量之间没有联系,以及它们的功能性质的其他方面,例如:形状差异(结构)也将被忽略。在本文中,我们研究了化学光谱数据集的一些高级表示形式,并为此比较了四个数据集的分类结果,分别使用它们的传统表示形式和其他两个表示形式:功能数据分析和相异性表示形式。这些方法可以考虑传统表示形式中缺少的信息,因此可以实现更好的分类结果。对于更适合用于化学光谱数据的相异性度量提出了一些建议。

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