Peak Criterion for Choosing Gaussian Kernel Bandwidth in Support Vector Data Description

TL;DR

This paper introduces an empirical criterion for selecting the optimal Gaussian kernel bandwidth in Support Vector Data Description, balancing boundary smoothness and data feature capture for improved outlier detection.

Contribution

It proposes a novel empirical method to choose Gaussian kernel bandwidth in SVDD, enhancing boundary quality and data representation.

Findings

The criterion effectively balances boundary smoothness and data feature preservation.

Application of the criterion results in improved outlier detection performance.

The method is adaptable to different data distributions.

Abstract

Support Vector Data Description (SVDD) is a machine-learning technique used for single class classification and outlier detection. SVDD formulation with kernel function provides a flexible boundary around data. The value of kernel function parameters affects the nature of the data boundary. For example, it is observed that with a Gaussian kernel, as the value of kernel bandwidth is lowered, the data boundary changes from spherical to wiggly. The spherical data boundary leads to underfitting, and an extremely wiggly data boundary leads to overfitting. In this paper, we propose empirical criterion to obtain good values of the Gaussian kernel bandwidth parameter. This criterion provides a smooth boundary that captures the essential geometric features of the data.