ML Anthology

LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection (Student Abstract)

Joseph A. Gallego-Mejia, Oscar A. Bustos-Brinez, Fabio A. González
AAAI 2023 pp. 16210-16211
doi:10.1609/AAAI.V37I13.26965 /aaai/2023/gallegomejia2023aaai-lean/

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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Cite

Text

Gallego-Mejia et al. "LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26965

Markdown

[Gallego-Mejia et al. "LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/gallegomejia2023aaai-lean/) doi:10.1609/AAAI.V37I13.26965

BibTeX

@inproceedings{gallegomejia2023aaai-lean,
  title     = {{LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection (Student Abstract)}},
  author    = {Gallego-Mejia, Joseph A. and Bustos-Brinez, Oscar A. and González, Fabio A.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16210-16211},
  doi       = {10.1609/AAAI.V37I13.26965},
  url       = {https://mlanthology.org/aaai/2023/gallegomejia2023aaai-lean/}
}