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.

本文研究使用不存在标签分类来进行异常检测，并设计了一种假设检验方法来排除仅有背景假设。通过测试两个数据集区域的统计独立性，我们能在不依赖定量异常得分或区域间背景估计外推的情况下排除仅有背景假设。我们的方法依赖于异常得分特征和数据集区域的条件独立性假设，这可以通过使用现有的去相关技术来保证。我们以 LHC Olympics 数据集为基准示例，证明互信息是测试统计独立性的合适方法，并且我们的方法即使在实际特征相关性存在的情况下，在不同信号分数下也表现出良好且稳健的性能。