Improving Interpretability of Scores in Anomaly Detection Based on
  Gaussian-Bernoulli Restricted Boltzmann Machine
Kaiji Sekimoto Muneki Yasuda
Gaussian-Bernoulli restricted Boltzmann machines (GBRBMs) are often used for
semi-supervised anomaly detection, where they are trained using only normal
data points. In GBRBM-based anomaly detection, normal and anomalous data are
classified based on a score that is identical to an energy function of the
marginal GBRBM. However, the classification threshold is difficult to set to an
appropriate value, as this score cannot be interpreted. In this study, we
propose a measure that improves score's interpretability based on its
cumulative distribution, and establish a guideline for setting the threshold
using the interpretable measure. The results of numerical experiments show that
the guideline is reasonable when setting the threshold solely using normal data
points. Moreover, because identifying the measure involves computationally
infeasible evaluation of the minimum score value, we also propose an evaluation
method for the minimum score based on simulated annealing, which is widely used
for optimization problems. The proposed evaluation method was also validated
using numerical experiments.