Anomaly Detection with Variance Stabilized Density Estimation
Amit Rozner Barak Battash Henry Li Lior Wolf Ofir Lindenbaum
Density estimation based anomaly detection schemes typically model anomalies
as examples that reside in low-density regions. We propose a modified density
estimation problem and demonstrate its effectiveness for anomaly detection.
Specifically, we assume the density function of normal samples is uniform in
some compact domain. This assumption implies the density function is more
stable (with lower variance) around normal samples than anomalies. We first
corroborate this assumption empirically using a wide range of real-world data.
Then, we design a variance stabilized density estimation problem for maximizing
the likelihood of the observed samples while minimizing the variance of the
density around normal samples. We introduce an ensemble of autoregressive
models to learn the variance stabilized distribution. Finally, we perform an
extensive benchmark with 52 datasets demonstrating that our method leads to
state-of-the-art results while alleviating the need for data-specific
hyperparameter tuning.