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首页> 外文期刊>Journal of near infrared spectroscopy >A note on Mahalanobis and related distance measures in WinISI and The Unscrambler
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A note on Mahalanobis and related distance measures in WinISI and The Unscrambler

机译：关于莫哈拉诺比斯的一份记录和WinIsi和Unscrambler的相关距离措施

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摘要

In identifying spectral outliers in near infrared calibration it is common to use a distance measure that is related to Mahalanobis distance. However, different software packages tend to use different variants, which lead to a translation problem if more than one package is used. Here the relationships between squared Mahalanobis distance D-2, the GH distance of WinISI, and the T-2 and leverage (L) statistics of The Unscrambler are established as D-2 = T-2 approximate to L x n approximate to GH x k, where n and k are the numbers of samples and variables, respectively, in the set of spectral data used to establish the distance measure. The implications for setting thresholds for outlier detection are discussed. On the way to this result the principal component scores from WinISI and The Unscrambler are compared. Both packages scale the scores for a component to have variances proportional to the contribution of that component to total variance, but the WinISI scores, unlike those from The Unscrambler, do not have mean zero.
机译：在识别近红外校准中的频谱异常值，通常使用与Mahalanobis距离相关的距离测量。但是，不同的软件包往往使用不同的变体，如果使用多个包，则会导致翻译问题。在这里，Squared Mahalanobis距离D-2之间的关系，WinISI的GH距离和T-2和杠杆（L）统计的unscrambler的统计数据被建立为D-2 = T-2近似于L Xn近似到GH XK，其中n和k分别在用于建立距离测量的频谱数据集中分别在用于建立距离测量的集合中的样本和变量的数量。讨论了用于设置异常值检测阈值的含义。在此结果的途中，比较了WinISI和解辨别者的主要成分分数。这两个包都缩放了一个组件的分数，以使与该组件的贡献成比例的差异与全方差的贡献成比例，但是与来自unscraMbler的那些不同的WinISI分数没有平均零。

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