PCA+SVM Source Code Implementation with MATLAB

This article provides a comprehensive guide to implementing data classification using PCA and SVM algorithms in MATLAB. Principal Component Analysis (PCA) serves as a feature extraction technique that transforms high-dimensional data into lower-dimensional representations, effectively reducing noise and redundancy in datasets. The Support Vector Machine (SVM) algorithm acts as a robust classifier capable of effectively separating data points into distinct categories. Our implementation follows a sequential workflow: first applying PCA for dimensionality reduction and feature selection, then utilizing SVM for classification on the transformed feature set. The MATLAB code includes key functions such as pca() for covariance matrix computation and eigenvalue decomposition, and fitcsvm() for training the classifier with optimized hyperparameters. We provide complete, well-commented MATLAB source code that enables readers to easily reproduce our experiments, analyze algorithm performance, and modify parameters for different datasets. The implementation demonstrates proper data preprocessing, PCA transformation matrix calculation, and SVM model training with cross-validation techniques.