MATLAB Implementation of Generalized Canonical Correlation Analysis (GCCA) for Multivariate Data Analysis

This MATLAB implementation of Generalized Canonical Correlation Analysis (GCCA) provides a comprehensive tool for multivariate data analysis applications such as feature dimensionality reduction, feature fusion, and correlation analysis. GCCA serves as a powerful methodology for analyzing relationships among multiple datasets, identifying their common underlying structures while preserving the unique characteristics of each individual dataset. The implementation includes core algorithmic components such as covariance matrix computation, eigenvalue decomposition, and canonical weight optimization. Key MATLAB functions utilized include eig() for eigenvalue problems and matrix operations for efficient multi-dataset processing. This code enables users to deepen their understanding of GCCA algorithms and master practical implementation techniques for data analysis within the MATLAB environment, featuring customizable parameters for different analysis scenarios and visualization capabilities for result interpretation.