Kernel Principal Component Analysis (Kernel PCA)
Kernel PCA Algorithm Implementation and Applications for Nonlinear Dimensionality Reduction
MATLAB Implementation of PCA Face Recognition with Complete Code Solution
This PCA face recognition program includes trained face dataset and demonstrates efficient implementation of Principal Component Analysis for face recognition. The code runs smoothly in MATLAB environment and provides high recognition accuracy through optimized feature extraction algorithms.
Principal Component Analysis Algorithm (PCA)
Principal Component Analysis (PCA) algorithm developed by international researchers, offering significant reference value with robust implementation
PCA-Based Face Recognition Method MATLAB Source Code
MATLAB source code for a Principal Component Analysis (PCA) based face recognition system - PCA_based Face Recognition System.rar, featuring dimensionality reduction and feature extraction algorithms
MATLAB Code Implementation for PCA Feature Extraction
Source code for PCA feature extraction, particularly useful for face recognition applications with algorithm implementation details
Face Recognition using PCA Implementation
A complete MATLAB-based face recognition program utilizing Principal Component Analysis (PCA) algorithm, featuring practical implementation with detailed code structure and mathematical foundation
Image Fusion with Three Algorithms: Weighted, IHS, and PCA
An image fusion program implementing three distinct fusion algorithms: Weighted, IHS, and PCA methods for multi-band image integration
ASM (Active Shape Model) - PCA-Based Deformable Models
ASM (Active Shape Model), Principal Component Analysis (PCA), and deformable models implementation. Using hand deformation as an example, this study includes 18 hand shapes with 72 landmarks each, performing Procrustes alignment followed by PCA analysis for statistical shape modeling.
Face Recognition System Based on PCA and SVM (MATLAB Implementation)
This MATLAB-based face recognition system utilizes PCA+SVM algorithms and includes executable source code with complete implementation details.
Principal Component Analysis (PCA) Method
Principal Component Analysis (PCA) is a dimensionality reduction technique based on the Karhunen-Loève (K-L) transform. The PCA algorithm identifies an optimal linear transformation matrix W according to specific performance criteria, enabling effective reduction of high-dimensional data while preserving maximum variance.