One class SVM model based on neural tangent kernel for anomaly detection task on small-scale data

Article type: Research Article

Authors: Zhai, Yuejing | Liu, Haizhong^{; *}

Affiliations: School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou, China

Correspondence: [*] Corresponding author. Haizhong Liu, School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou 730070, China. Tel.: +86 13919328623; E-mail: [email protected].

Abstract: Recent studies have shown that the evolution of infinitely wide neural networks satisfying certain conditions can be described by a kernel function called neural tangent kernel (NTK). We introduce NTK into a one-class support vector machine model and select data from different domains in UCI for a small-sample outlier detection task, demonstrate that NTK-OCSVM generally outperforms a variety of commonly used classification models, with more than 20% improvement in accuracy for similar models. When the kernel function parameters are varied, the experiments show that the model has strong robustness within a certain parameter range. Finally, we experimentally compare the time complexity of different models and the decision boundaries, and demonstrate that NTK-OCSVM improves accuracy at the expense of operational efficiency and has linear decision boundaries.

Keywords: One class SVM, neural tangent kernel, anomaly detection

DOI: 10.3233/JIFS-213088

Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2731-2746, 2022