Peak Criterion for Choosing Gaussian Kernel Bandwidth in Support Vector Data Description
This work addresses a specific parameter tuning issue in SVDD for outlier detection, representing an incremental improvement.
The paper tackles the problem of selecting the Gaussian kernel bandwidth in Support Vector Data Description (SVDD) to avoid underfitting or overfitting, proposing an empirical criterion that yields a smooth boundary capturing essential data geometry.
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.