KRX的裂缝:当距离越远的点越不异常

2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) Pub Date : 2016-08-01 DOI:10.1109/WHISPERS.2016.8071717

J. Theiler, G. Grosklos

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引用次数: 2

摘要

我们研究了基于Mahalanobis-distance的核rx (KRX)算法用于异常检测，发现它会出现一个不幸的现象:对于远离训练数据的点，异常会随着距离的增加而减少。我们在一些特殊情况下直接演示了这一点，并提供了适用于大带宽制度的更一般的论点。

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Cracks in KRX: When more distant points are less anomalous

We examine the Mahalanobis-distance based kernel-RX (KRX) algorithm for anomaly detection, and find that it can exhibit an unfortunate phenomenon: the anomalousness, for points far from the training data, can decrease with increasing distance. We demonstrate this directly for a few special cases, and provide a more general argument that applies in the large bandwidth regime.

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2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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