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Kernel dependence analysis and graph structure morphing for novelty detection with high-dimensional small size data set
Mechanical Systems and Signal Processing ( IF 7.9 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.ymssp.2020.106775
Reza Mohammadi-Ghazi , Roy E. Welsch , Oral Büyüköztürk

Abstract In this study, we propose a new approach for novelty detection that uses kernel dependence techniques for characterizing the statistical dependencies of random variables (RV) and use this characterization as a basis for making inference. Considering the statistical dependencies of the RVs in multivariate problems is an important challenge in novelty detection. Ignoring these dependencies, when they are strong, may result in inaccurate inference, usually in the form of high false positive rates. Previously studied methods, such as graphical models or conditional classifiers, mainly use density estimation techniques as their main learning element to characterize the dependencies of the relevant RVs. Therefore, they suffer from the curse of dimensionality which makes them unable to handle high-dimensional problems. The proposed method, however, avoids using density estimation methods, and rather, employs a kernel method, which is robust with respect to dimensionality, to encode the dependencies and hence, it can handle problems with arbitrarily high-dimensional data. Furthermore, the proposed method does not need any prior information about the dependence structure of the RVs; thus, it is applicable to general novelty detection problems with no simplifying assumption. To test the performance of the proposed method, we apply it to realistic application problems for analyzing sensor networks and compare the results to those obtained by peer methods.

中文翻译:

用于高维小尺寸数据集新颖性检测的内核依赖分析和图结构变形

摘要 在这项研究中,我们提出了一种新颖性检测的新方法,该方法使用内核依赖技术来表征随机变量 (RV) 的统计依赖关系,并将这种表征作为进行推理的基础。在多变量问题中考虑 RV 的统计依赖性是新颖性检测中的一个重要挑战。忽略这些依赖性,当它们很强时,可能会导致推断不准确,通常表现为高误报率。先前研究的方法,例如图形模型或条件分类器,主要使用密度估计技术作为其主要学习元素来表征相关 RV 的依赖关系。因此,他们遭受维数诅咒,这使他们无法处理高维问题。建议的方法,然而,避免使用密度估计方法,而是采用在维度方面具有鲁棒性的核方法来对依赖项进行编码,因此它可以处理任意高维数据的问题。此外,所提出的方法不需要任何关于 RV 依赖结构的先验信息;因此,它适用于没有简化假设的一般新颖性检测问题。为了测试所提出方法的性能,我们将其应用于分析传感器网络的实际应用问题,并将结果与​​对等方法获得的结果进行比较。此外,所提出的方法不需要任何关于 RV 依赖结构的先验信息;因此,它适用于没有简化假设的一般新颖性检测问题。为了测试所提出方法的性能,我们将其应用于分析传感器网络的实际应用问题,并将结果与​​对等方法获得的结果进行比较。此外,所提出的方法不需要任何关于 RV 依赖结构的先验信息;因此,它适用于没有简化假设的一般新颖性检测问题。为了测试所提出方法的性能,我们将其应用于分析传感器网络的实际应用问题,并将结果与​​对等方法获得的结果进行比较。
更新日期:2020-09-01
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