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 pseudo-abnormal sample


Unsupervised Anomaly Detection in The Presence of Missing Values

Neural Information Processing Systems

In this work, first, we construct and evaluate a straightforward strategy, "impute-then-detect", via combining state-of-the-art imputation methods with unsupervised anomaly detection methods, where the training data are composed of normal samples only.