Joseph A Gallego-Mejia, Fabio A González
*arXiv preprint arXiv:2211.08525

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.

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📌 BibTeX citation

@article{gallego2022lean,
  title={LEAN-DMKDE: quantum latent density estimation for anomaly detection},
  author={Gallego-Mejia, Joseph and Bustos-Brinez, Oscar and Gonz{\'a}lez, Fabio A},
  journal={arXiv preprint arXiv:2211.08525},
  year={2022}
}
@inproceedings{gallego2023lean,
  title={LEAN-DMKDE: quantum latent density estimation for anomaly detection (student abstract)},
  author={Gallego-Mejia, Joseph A and Bustos-Brinez, Oscar A and Gonz{\'a}lez, Fabio A},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  number={13},
  pages={16210--16211},
  year={2023}
}