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We present a novel algorithm for anomaly detection on very large datasets and data streams. The method, named EXPected Similarity Estimation (expose), is kernel-based and able to efficiently compute the similarity between new data points and the distribution of regular data. The estimator is formulated as an inner product with a reproducing kernel Hilbert space embedding and makes no assumption about...
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