Implementation of Linear Discriminant Function Classification in MATLAB

This MATLAB program presents a comprehensive implementation of linear discriminant function classification. We begin by explaining the fundamental principles of linear discriminant analysis (LDA) and its practical applications in pattern recognition. The implementation includes systematic data preprocessing routines using MATLAB's built-in functions like zscore for normalization, followed by feature extraction techniques that optimize class separability. The core algorithm implements Fisher's linear discriminant through covariance matrix calculations and eigenvalue decomposition, with explicit code sections for training the classifier using optimum projection vectors.

The program structure clearly separates data loading, preprocessing, model training, and testing phases, utilizing key MATLAB functions such as fitcdiscr for discriminant analysis and predict for classification validation. Each code segment contains detailed comments explaining the mathematical operations, including within-class and between-class scatter matrix computations. Through this hands-on implementation, users will gain practical understanding of hyperplane optimization and decision boundary formation in feature space.

This educational resource provides complete MATLAB code with performance evaluation metrics, including confusion matrix generation and accuracy calculations. For any technical inquiries or suggestions regarding the implementation details, please feel free to contact us for further discussion.