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Anomaly-Specified Virtual Dimensionality

机译：异常指定的虚拟维数

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Virtual dimensionality (VD) has received considerable interest where VD is used to estimate the number of spectral distinct signatures, denoted by p. Unfortunately, no specific definition is provided by VD for what a spectrally distinct signature is. As a result, various types of spectral distinct signatures determine different values of VD. There is no one value-fit-all for VD. In order to address this issue this paper presents a new concept, referred to as anomaly-specified VD (AS-VD) which determines the number of anomalies of interest present in the data. Specifically, two types of anomaly detection algorithms are of particular interest, sample covariance matrix K-based anomaly detector developed by Reed and Yu, referred to as K-RXD and sample correlation matrix R-based RXD, referred to as R-RXD. Since K-RXD is only determined by 2nd order statistics compared to R-RXD which is specified by statistics of the first two orders including sample mean as the first order statistics, the values determined by K-RXD and R-RXD will be different. Experiments are conducted in comparison with widely used eigen-based approaches.
机译：虚拟维数（VD）引起了极大的兴趣，其中VD用于估计频谱不同特征的数量，用p表示。不幸的是，VD没有提供什么光谱上不同的特征的特定定义。结果，各种类型的光谱不同特征确定了VD的不同值。 VD没有一个万能的解决方案。为了解决这个问题，本文提出了一个新的概念，称为异常指定VD（AS-VD），它确定了数据中存在的异常数量。具体来说，两种类型的异常检测算法特别受关注，一种是由Reed和Yu开发的基于样本协方差矩阵K的异常检测器，称为K-RXD，另一种是基于样本相关矩阵R的RXD，称为R-RXD。由于K-RXD仅由2阶统计量确定，而R-RXD由包括样本均值在内的前两个阶数的统计量指定的R-RXD进行比较，因此由K-RXD和R-RXD确定的值将不同。与广泛使用的基于特征的方法进行了比较。

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