Novelty detection

Novelty detection

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.

References

  1. ^ a b M. Markou, S. Singh, Novelty detection: A review, part 1: Statistical approaches, Signal Processing 83, 2481–2497, 2003



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