2DPCA: A Novel Dimensionality Reduction Method
2DPCA is an improved dimensionality reduction method based on traditional PCA, featuring innovative approaches and well worth exploring for enhanced data processing capabilities.
MATLAB Implementation of Principal Component Analysis (PCA) with Code Examples
A comprehensive MATLAB implementation of PCA algorithm, featuring detailed code explanations, data visualization techniques, and dimensionality reduction demonstrations for machine learning applications.
MATLAB Code Implementation of PCA Steps
PCA Implementation Steps: 1. Center the data (mean normalization); 2. Compute the covariance matrix; 3. Calculate eigenvalues and eigenvectors of the covariance matrix; 4. Sort eigenvalues and corresponding eigenvectors; 5. Determine projection direction based on target dimensionality d'; 6. Compute dimensionally reduced data
PCA Principal Component Analysis Algorithm Implementation
MATLAB source code for PCA principal component analysis algorithm, implementing PCA using MATLAB with detailed code explanations and practical applications.
PCA (Principal Component Analysis) - MATLAB Source Code with Implementation Examples
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.
PCA-based Remote Sensing Image Fusion
PCA-based remote sensing image fusion with excellent results, suitable as introductory material for learning remote sensing image fusion techniques, featuring implementation insights about principal component analysis and image processing workflows.
MATLAB Implementation of Face Recognition System
This face recognition program performs image preprocessing followed by feature extraction using Principal Component Analysis (PCA). The implementation includes histogram equalization, dimensionality reduction, and classification algorithms.
PCA-Based Remote Sensing Image Fusion
MATLAB implementation of PCA-based remote sensing image fusion algorithm, fully compatible with MATLAB 2011b, featuring multi-band image integration and enhanced visualization capabilities.
MATLAB Toolkit for Common Feature Extraction Methods
A comprehensive MATLAB toolkit containing implementations of essential feature extraction algorithms including PCA, CCA, MNF, PLS, KPCA, KCCA, KMNF, and KPLS with complete source code and mathematical formulations.
Principal Component Analysis for Facial Feature Recognition
Implementation of PCA, Fisher Linear Discriminant, Kernel PCA (KPCA), and 2D Discrete Wavelet Transform (DWT2) for facial recognition systems with dimensionality reduction techniques