Novelty detection is the identification of new or unknown data or signals that a machine learning system is not aware of during training[1]. Novelty detection is one-class classification. The known data form one class, and a novelty-detection method tries to identify outliers that differ from the distribution of ordinary data, which formed the single data class[1]. Compared to multi-class classification, one-class classification is useful if outliers are sparse compared to ordinary data.