Finally, we benchmark the performance of the SVDD against a simpler anomaly detection algorithm, the elliptic envelope (EE). The EE assumes: (1) the underlying distribution of the normal response vectors is Gaussian, with an unknown mean vector m and covariance matrix Σ and (2) We use the EE implementation EllipticEnvelope in scikit-learn and tune the contamination hyperparameter using the same procedure we use to tune γ and ν for our SVDD (see Sec. 4.3.2).