MATLAB Toolbox for SVDD Implementation with Code Examples

The MATLAB Support Vector Data Description (SVDD) toolbox is a specialized tool designed for one-class classification problems, building upon support vector machine principles. It's particularly suitable for anomaly detection and data boundary description scenarios. The implementation typically involves creating a class structure with methods for model training, prediction, and visualization.

The core objective of SVDD is to find a minimal hypersphere that encompasses most target data points during training, while positioning outliers outside this boundary. This method proves especially valuable when dealing with complex data distributions or situations lacking sufficient anomaly samples. The algorithm implementation involves solving a quadratic optimization problem using MATLAB's quadprog function or custom optimization routines.

Key features of the toolbox include: Model Training: Fit SVDD models using training data to determine optimal hypersphere parameters (center and radius). The code typically includes data preprocessing, parameter initialization, and iterative optimization loops. Boundary Optimization: Handle non-linearly separable data through kernel functions (such as Gaussian RBF kernel) to improve classification accuracy. The kernel matrix computation is optimized using vectorized operations for efficiency. Prediction and Evaluation: Classify new data points by calculating their distance to the hypersphere center and comparing against the trained radius. The toolbox provides anomaly scores and confidence metrics through dedicated scoring functions.

The SVDD toolbox's advantages lie in its flexibility and robustness, making it widely applicable for one-class classification problems in industrial inspection, network security, and other domains. The code structure allows for easy customization of kernel parameters and optimization tolerances through configurable input arguments.