Explore MATLAB source code curated for "特征降维" with clean implementations, documentation, and examples.
MATLAB implementation of canonical correlation analysis for multivariate data processing including feature dimensionality reduction, feature fusion, and correlation analysis, featuring configurable parameters and modular code structure.
MATLAB code implementation of Generalized Canonical Correlation Analysis (GCCA) for multivariate data analysis tasks including feature dimensionality reduction, feature fusion, and correlation analysis, with enhanced algorithmic explanations and function descriptions.
MATLAB code implementation of Fisher Linear Discriminant Analysis (FLDA) for multivariate data analysis, including feature dimensionality reduction, feature fusion, and correlation analysis with detailed algorithm explanations and computational implementation.
MATLAB implementation of PCA (Principal Component Analysis) algorithm featuring comprehensive test examples and documentation, specifically designed for feature dimension reduction in image classification tasks. The package includes detailed explanations of covariance matrix computation, eigenvalue decomposition, and principal component extraction.
Canonical Correlation Analysis code for multivariate data processing techniques including feature dimensionality reduction, feature fusion, and correlation analysis (commonly used mathematical modeling algorithms)
MATLAB code implementation of Discriminative Canonical Correlation Analysis (DCCA) for multivariate data analysis including feature dimensionality reduction, feature fusion, and correlation analysis.
PCA and SVM for Face Recognition: A Comprehensive Approach with Feature Reduction and Classification Algorithms
MATLAB implementation for feature dimensionality reduction techniques including PCA, SFFS, SBS, and SFS with code examples and algorithm explanations.
Implementation of 2D-PCA for Face Recognition with ORL Database