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

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.

Cited by in corpus (6)

• The Mean and Median Criterion for Automatic Kernel Bandwidth Selection for Support Vector Data Description
• Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel
• Kernel Bandwidth Selection for SVDD: Peak Criterion Approach for Large Data
• A Non-Parametric Control Chart For High Frequency Multivariate Data
• A New SVDD-Based Multivariate Non-parametric Process Capability Index
• The Trace Criterion for Kernel Bandwidth Selection for Support Vector Data Description