Anomaly Detection with Variance Stabilized Density Estimation

arXiv:2306.00582v2 Announce Type: replace
Abstract: We propose a modified density estimation problem that is highly effective for detecting anomalies in tabular data. Our approach assumes that the density function is relatively stable (with lower variance) around normal samples. We have verified this hypothesis empirically using a wide range of real-world data. Then, we present a variance-stabilized density estimation problem for maximizing the likelihood of the observed samples while minimizing the variance of the density around normal samples. To obtain a …

abstract anomaly anomaly detection arxiv cs.ai cs.lg data detection function hypothesis normal samples tabular tabular data type variance world