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机器学习路径的好功能

[英]Good features of paths for machine learning

I'm looking into ML problems (mostly density estimation and anomaly detection) with paths made up of coordinates (GPS). 我正在研究ML问题(主要是密度估计和异常检测),路径由坐标(GPS)组成。 Other than the coordinates themselves and deltas (changes between adjacent coordinate points) and polar coordinates what are some other good features? 除了坐标本身和增量(相邻坐标点之间的变化)和极坐标之外还有哪些其他好的特征? What features make intuitive attributes like straightness, curvy-ness, smoothness, and loopy-ness explicit? 什么功能使直观,弯曲,平滑和循环显式等直观属性?

For straightness/curviness you may want to calculate an approximate first derivative of the curve, for smoothness the second and higher derivatives. 对于直线度/曲率,您可能想要计算曲线的近似一阶导数,以获得平滑度的二阶和更高阶导数。 If by loopiness you mean the tendency to return to places several times, you could for instance count how many segments intersect each other. 如果通过循环表示多次返回场所的倾向,您可以计算相互交叉的段数。

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