In many gas sensing tasks, we simply wish to become aware of gas compositions that deviate from normal conditions in the environment. More, there may be numerous diverse and unforeseen mechanisms (malfunctions of equipment, leaking pipes, exogenous introduction of vapors, etc.) that lead to such deviations. Consequently, collecting the response of a sensor array to anomalous gas compositions for training a supervised classifier (normal vs. anomalous) is difficult.