A New Anomaly Detection Algorithm Based on Quantum Mechanics
A New Anomaly Detection Algorithm Based on Quantum Mechanics
复制标题
一种基于量子力学的新型异常检测算法
DOI：
10.1109/icdm.2012.127
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Dantong Yu
中科院分区：
文献类型：
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作者：
Hao Huang;Hong Qin;Shinjae Yoo;Dantong Yu
本文的主要创新之处在于，我们首次尝试将量子力学理论应用于高维数据挖掘数据集的异常（离群）检测。我们提出了费米密度描述符（FDD），它代表了测量的费米子在一个特定的位置异常检测的概率。我们还量化和检查不同的拉普拉斯归一化效果，并选择最好的异常检测。理论证明和定量实验都表明，我们提出的FDD是更大的歧视性和鲁棒性比常用的算法。
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.
基于多尺度热核的体积形态特征。
DOI：
10.1007/978-3-319-24574-4_90
发表时间：
2015
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
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作者：
Wang,Gang;Wang,Yalin
通讯作者：
Wang,Yalin