MATLAB Program for Principal Component Analysis with Contribution Rate Output and 2D Scatter Plot Visualization

This MATLAB program implements Principal Component Analysis (PCA), a widely used dimensionality reduction technique that transforms high-dimensional data into lower-dimensional representations while minimizing information loss. The program employs covariance matrix computation and eigenvalue decomposition to extract principal components, with automated calculation of contribution rates for each component to help users understand data distribution patterns. Key implementation features include: - Automated standardization of input data using z-score normalization - Eigenvalue decomposition of covariance matrix to extract principal components - Calculation of cumulative contribution rates through eigenvalue ratios - Projection of original data onto principal component space using linear transformations The program provides visualization capabilities by generating 2D scatter plots that display data distribution in the principal component coordinate system, enabling intuitive observation and analysis of data patterns. The implementation includes customizable plotting parameters for adjusting marker styles, colors, and axis labels to enhance visual clarity. This robust and user-friendly tool serves various PCA applications, featuring modular code structure with separate functions for data preprocessing, PCA computation, and visualization. The program handles different data formats and includes error checking for matrix dimensions and data validity, making it suitable for diverse principal component analysis requirements in research and industrial applications.