MATLAB Implementation of Discriminative Canonical Correlation Analysis for Feature Dimensionality Reduction

This MATLAB implementation provides Discriminative Canonical Correlation Analysis (DCCA) for multivariate data analysis. DCCA serves as a powerful analytical tool particularly useful for feature dimensionality reduction, feature fusion, and correlation analysis. The algorithm helps researchers better understand relationships within datasets, thereby enhancing data modeling and predictive analysis capabilities. The code implementation includes core DCCA functions that calculate canonical correlations between datasets while incorporating discriminative information to improve feature separation. Key components feature eigenvalue decomposition for optimal projection vectors and covariance matrix calculations for relationship analysis between variable sets. Users can easily perform DCCA analysis with default parameters while having the flexibility to modify the code for customized analysis and data visualization. The implementation supports matrix operations for efficient computation and includes plotting functions for result visualization. Through this implementation, researchers can efficiently reduce feature dimensions while preserving discriminative information, merge features from different modalities, and analyze complex correlations between multivariate datasets. The code structure allows for straightforward integration with existing MATLAB workflows and customization for specific research requirements.