Outlier detection, also called anomaly detection, is an important issue in pattern recognition and knowledge discovery. Previous outlier detection methods can not effectively control the false-alarm probability. To solve the problem, a supervised method based on Normalized Residual (NR) is proposed. Using the training patterns, it first calculates the NR value of the query pattern, which is compared with a predefined detection threshold to determine whether the pattern is an outlier. In this paper, the relationship between the threshold and false-alarm probability is theoretically derived, based on which an appropriate threshold can be chosen. In this way, the desired false-alarm probability can be obtained even when few training patterns are available. Simulations and measured data experiments validate the superior performance of the proposed method on outlier detection and false-alarm probability controlling.
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