AAAI 2023technical0 citations
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection (Student Abstract)
Joseph A. Gallego-Mejia, Oscar A. Bustos-Brinez, Fabio A. González
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, 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.
BibTeX
@article{Gallego-Mejia_Bustos-Brinez_González_2024, title={LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26965}, DOI={10.1609/aaai.v37i13.26965}, abstractNote={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, 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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Gallego-Mejia, Joseph A. and Bustos-Brinez, Oscar A. and González, Fabio A.}, year={2024}, month={Jul.}, pages={16210-16211} }