A new anomaly detection algorithm based on quantum mechanics

Hao Huang; Hong Qin; Shinjae Yoo; Dantong Yu

doi:10.1109/ICDM.2012.127

A new anomaly detection algorithm based on quantum mechanics

Hao Huang, Hong Qin, Shinjae Yoo, Dantong Yu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 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 language	English (US)
Title of host publication	Proceedings - 12th IEEE International Conference on Data Mining, ICDM 2012
Pages	900-905
Number of pages	6
DOIs	https://doi.org/10.1109/ICDM.2012.127
State	Published - Dec 1 2012
Externally published	Yes
Event	12th IEEE International Conference on Data Mining, ICDM 2012 - Brussels, Belgium Duration: Dec 10 2012 → Dec 13 2012

Other

Other	12th IEEE International Conference on Data Mining, ICDM 2012
Country/Territory	Belgium
City	Brussels
Period	12/10/12 → 12/13/12

All Science Journal Classification (ASJC) codes

Engineering(all)

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10.1109/ICDM.2012.127

Cite this

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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.",

author = "Hao Huang and Hong Qin and Shinjae Yoo and Dantong Yu",

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AU - Qin, Hong

AU - Yoo, Shinjae

AU - Yu, Dantong

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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BT - Proceedings - 12th IEEE International Conference on Data Mining, ICDM 2012

T2 - 12th IEEE International Conference on Data Mining, ICDM 2012

Y2 - 10 December 2012 through 13 December 2012

ER -

A new anomaly detection algorithm based on quantum mechanics

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