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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Novelty Detection Using Level Set Methods
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Novelty Detection Using Level Set Methods

机译：使用水平集方法进行新颖性检测

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摘要

This paper presents a level set boundary description (LSBD) approach for novelty detection that treats the nonlinear boundary directly in the input space. The proposed approach consists of level set function (LSF) construction, boundary evolution, and termination of the training process. It employs kernel density estimation to construct the LSF of the initial boundary for the training data set. Then, a sign of the LSF-based algorithm is proposed to evolve the boundary and make it fit more tightly in the data distribution. The training process terminates when an expected fraction of rejected normal data is reached. The evolution process utilizes the signs of the LSF values at all training data points to decide whether to expand or shrink the boundary. Extensive experiments are conducted on benchmark data sets to evaluate the proposed LSBD method and compare it against four representative novelty detection methods. The experimental results demonstrate that the novelty detector modeled with the proposed LSBD can effectively detect anomalies.
机译：本文提出了一种用于新颖性检测的水平集边界描述（LSBD）方法，该方法直接在输入空间中处理非线性边界。所提出的方法包括水平集功能（LSF）的构造，边界演化和训练过程的终止。它采用核密度估计为训练数据集构造初始边界的LSF。然后，提出了一种基于LSF的算法的符号来扩展边界，使其更紧密地适合数据分布。当达到拒绝的正常数据的预期比例时，训练过程终止。演化过程利用所有训练数据点的LSF值的符号来决定是扩大还是缩小边界。在基准数据集上进行了广泛的实验，以评估所提出的LSBD方法并将其与四种代表性的新颖性检测方法进行比较。实验结果表明，以所提出的LSBD建模的新颖性检测器可以有效地检测异常。

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