论文标题
零假设检测检测
Null Hypothesis Test for Anomaly Detection
论文作者
论文摘要
我们扩展了无标记检测分类的使用，其假设检验旨在排除仅背景假设。通过测试两个区分数据集区域的统计独立性，我们可以排除仅背景假设的情况，而无需依赖固定的区域分数削减或区域之间背景估计的外推。该方法依赖于假设异常得分特征和数据集区域的条件独立性，可以使用现有的去相关技术来确保。作为基准示例，我们考虑了LHC奥运会数据集，在其中我们表明，共同信息代表了统计独立性的合适测试，即使在存在逼真的特征相关性的情况下，我们的方法在不同的信号分数上也表现出出色而稳健的性能。
We extend the use of Classification Without Labels for anomaly detection with a hypothesis test designed to exclude the background-only hypothesis. By testing for statistical independence of the two discriminating dataset regions, we are able to exclude the background-only hypothesis without relying on fixed anomaly score cuts or extrapolations of background estimates between regions. The method relies on the assumption of conditional independence of anomaly score features and dataset regions, which can be ensured using existing decorrelation techniques. As a benchmark example, we consider the LHC Olympics dataset where we show that mutual information represents a suitable test for statistical independence and our method exhibits excellent and robust performance at different signal fractions even in presence of realistic feature correlations.
