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Quantum Machine Learning Quantum Foundations

LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection

arXiv

Authors: Joseph Gallego-Mejia, Oscar Bustos-Brinez, Fabio A. González

Year

2022

Paper ID

57298

Status

Preprint

Abstract Read

~2 min

Abstract Words

103

Citations

N/A

Abstract

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 evaluation was performed on different benchmark datasets. The experimental results show that the method performs on par with or outperforms other state-of-the-art methods.

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This paper presents an anomaly detection model that combines the strong statistical foundation of density-estimation-based anomaly detection methods with the...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2211.08525 [2] arXiv https://arxiv.org/abs/2211.08525 [3] Publisher https://arxiv.org/abs/2211.08525

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