A New Anomaly Detection Algorithm Based on Quantum Mechanics
Hao Huang, Hong Ying Qin, Shinjae Yoo, Dantong Yu · 2012
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 high-dimensional 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.