Measuring Abnormality in High Dimensional Spaces with Applications in Biomechanical Gait Analysis
Measuring Abnormality in High Dimensional Spaces with Applications in Biomechanical Gait Analysis
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DOI：
10.1038/s41598-018-33694-3
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发表时间：
2018-10-19
影响因子：
4.6
通讯作者：
Wyatt, Marilynn
中科院分区：
文献类型：
--
作者：
Marks, Michael;Kingsbury, Trevor;Wyatt, Marilynn
使用高维数据准确测量受试者的异常情况可以促进更好的结果研究。利用仪器步态分析中的应用，本文演示了如何使用本质上非独立的数据来测量整体异常可能会导致结果偏差。然后介绍了一种方法来解决这种偏差并准确测量高维空间中的异常。虽然这种方法与以前的文献一致，但它在两个主要方面有所不同。有利的是，它可以应用于观测数量少于特征/变量数量的数据集，并且它可以抽象到几乎任何数量的域或维度。这些方法的初步结果表明，它们可以检测到已知的、真实世界中受试者群体之间的异常差异，而现有的测量方法无法做到这一点。该方法通过CRAN上的异常R包免费提供。
Accurately measuring a subject's abnormality using high dimensional data can empower better outcomes research. Utilizing applications in instrumented gait analysis, this article demonstrates how using data that is inherently non-independent to measure overall abnormality may bias results. A methodology is then introduced to address this bias and accurately measure abnormality in high dimensional spaces. While this methodology is in line with previous literature, it differs in two major ways. Advantageously, it can be applied to datasets in which the number of observations is less than the number of features/variables, and it can be abstracted to practically any number of domains or dimensions. Initial results of these methods show that they can detect known, real-world differences in abnormality between subject groups where established measures could not. This methodology is made freely available via the abnormality R package on CRAN.