@inproceedings{8466f269728d4c8d99ff5c3fab5a18ad,
title = "A new anomaly detection algorithm based on quantum mechanics",
abstract = "The primary originality of this paper lies at the fact that we have made the first attempt to apply quantum mechanics theory to anomaly (outlier) detection in highdimensional datasets for data mining. We propose Fermi Density Descriptor (FDD) which represents the probability of measuring a fermion at a specific location for anomaly detection. We also quantify and examine different Laplacian normalization effects and choose the best one for anomaly detection. Both theoretical proof and quantitative experiments demonstrate that our proposed FDD is substantially more discriminative and robust than the commonly-used algorithms.",
keywords = "Anomaly detection, Quantum mechanics",
author = "Hao Huang and Hong Qin and Shinjae Yoo and Dantong Yu",
year = "2012",
doi = "10.1109/ICDM.2012.127",
language = "English (US)",
isbn = "9780769549057",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
pages = "900--905",
booktitle = "Proceedings - 12th IEEE International Conference on Data Mining, ICDM 2012",
note = "12th IEEE International Conference on Data Mining, ICDM 2012 ; Conference date: 10-12-2012 Through 13-12-2012",
}