A new anomaly detection algorithm based on quantum mechanics

Hao Huang, Hong Qin, Shinjae Yoo, Dantong Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - 12th IEEE International Conference on Data Mining, ICDM 2012
Pages900-905
Number of pages6
DOIs
StatePublished - Dec 1 2012
Externally publishedYes
Event12th IEEE International Conference on Data Mining, ICDM 2012 - Brussels, Belgium
Duration: Dec 10 2012Dec 13 2012

Other

Other12th IEEE International Conference on Data Mining, ICDM 2012
CountryBelgium
CityBrussels
Period12/10/1212/13/12

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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