Pattern Recognition Assignment - Fully Custom Simulation

In this paper, I implemented a fully custom simulation program for a pattern recognition assignment. The implementation workflow begins with applying Principal Component Analysis (PCA) to the IRIS dataset for dimensionality reduction, where the PCA algorithm computes eigenvectors and eigenvalues to project data onto principal components. Subsequently, the reduced-dimensional data is classified using the minimum error method, which implements Bayesian decision theory to minimize classification error rates. The accompanying compressed package contains my MATLAB source code (featuring functions like pca() for covariance matrix computation and classification algorithms), a self-written technical report, and the IRIS dataset in .MAT format for program loading. The source code includes comprehensive comments explaining each algorithmic step and matrix operation to facilitate understanding of the implementation process. Final classification results, including accuracy metrics and confusion matrices, are exported to a text file for further analysis. Beyond implementation specifics, I elaborated on the project's background and significance. Through this project, I gained deep insights into pattern recognition fundamentals and practical applications of PCA algorithms, while enhancing my programming skills and data analysis capabilities. The implementation demonstrates proper handling of covariance matrix calculation, eigenvalue decomposition, and decision boundary formation. I believe this program not only serves as an academic achievement but also provides valuable reference material for others studying pattern recognition techniques and MATLAB implementation approaches.