LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection (Student Abstract)
Keywords:Anomaly Detection, Deep Learning, Density Matrix, Random Features, Quantum Machine Learning
AbstractThis 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, that learns a low-dimensional representation of the data, with a density-estimation model based on density matrices in an end-to-end architecture that can be trained using gradient-based optimization techniques. A systematic experimental evaluation was performed on different benchmark datasets. The experimental results show that the method is able to outperform other state-of-the-art methods.
How to Cite
Gallego-Mejia, J. A., Bustos-Brinez, O. A., & González, F. A. (2023). LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16210-16211. https://doi.org/10.1609/aaai.v37i13.26965
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