论文标题
红色熊猫：通过消除滋扰因素消除异常检测
Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors
论文作者
论文摘要
异常检测方法努力以语义方式发现与规范不同的模式。这个目标是模棱两可的，因为数据点与规范不同的属性不同，例如年龄，种族或性别可能被某些操作员视为异常，而其他操作员可能认为这种属性无关紧要。从先前的研究中断，我们提出了一种新的异常检测方法，该方法允许操作员将属性排除在被认为与异常检测相关的情况下。然后，我们的方法学习了不包含有关滋扰属性的信息的表示形式。使用基于密度的方法进行异常评分。重要的是，我们的方法不需要指定与检测异常相关的属性，这在异常检测中通常是不可能的，而是只能忽略的属性。提出了一项实证研究，以验证我们方法的有效性。
Anomaly detection methods strive to discover patterns that differ from the norm in a semantic way. This goal is ambiguous as a data point differing from the norm by an attribute e.g., age, race or gender, may be considered anomalous by some operators while others may consider this attribute irrelevant. Breaking from previous research, we present a new anomaly detection method that allows operators to exclude an attribute from being considered as relevant for anomaly detection. Our approach then learns representations which do not contain information over the nuisance attributes. Anomaly scoring is performed using a density-based approach. Importantly, our approach does not require specifying the attributes that are relevant for detecting anomalies, which is typically impossible in anomaly detection, but only attributes to ignore. An empirical investigation is presented verifying the effectiveness of our approach.
