LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection

• URL: http://arxiv.org/abs/2211.08525v1
• Date: Tue, 15 Nov 2022 21:51:42 GMT
• Title: LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection
• Authors: Joseph Gallego-Mejia, Oscar Bustos-Brinez, Fabio A. Gonz\'alez
• Abstract summary: The method combines an autoencoder, for learning a low-dimensional representation of the data, with a density-estimation model. The method predicts a degree of normality for new samples based on the estimated density. The experimental results show that the method performs on par with or outperforms other state-of-the-art methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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