LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection. (arXiv:2211.08525v1 [cs.LG])

This paper presents an anomaly detection model that combines the strong
statistical foundation of density-estimation-based anomaly detection methods
with the representation-learning ability of deep-learning models. The method
combines an autoencoder, for learning a low-dimensional representation of the
data, with a density-estimation model based on random Fourier features and
density matrices in an end-to-end architecture that can be trained using
gradient-based optimization techniques. The method predicts a degree of
normality for new samples based on the estimated density. A systematic
experimental …

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