{"title":"Cracks in KRX: When more distant points are less anomalous","authors":"J. Theiler, G. Grosklos","doi":"10.1109/WHISPERS.2016.8071717","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WHISPERS.2016.8071717","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
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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