Implementation and Demonstration of Principal Component Analysis Algorithm Using MATLAB Code

In this paper, we will develop MATLAB programs to compute and demonstrate the Principal Component Analysis (PCA) algorithm. We begin by introducing the fundamental concepts of PCA and its applications in data analysis. Subsequently, we provide a detailed discussion on implementing the PCA algorithm using MATLAB programming, covering key computational steps including data standardization, covariance matrix calculation, eigenvalue decomposition, and principal component projection. We will explain critical MATLAB functions such as zscore() for data normalization, cov() for covariance computation, and eig() for eigenvalue extraction. Additionally, we share useful techniques and methodologies to enhance understanding and practical application of PCA, including variance explanation analysis and component selection criteria. Finally, we present practical code examples demonstrating algorithm implementation scenarios, such as dimensionality reduction and feature extraction for multivariate datasets. Through this paper, you will learn to implement PCA using MATLAB and understand its broad applications in data analytics through hands-on programming examples. Key implementation aspects covered: - Data preprocessing and standardization techniques - Covariance matrix computation and eigenvalue decomposition - Principal component selection based on variance thresholds - Visualization methods for PCA results - Integration with MATLAB's statistical and plotting functions