Explore MATLAB source code curated for "PCA算法" with clean implementations, documentation, and examples.
The PCA algorithm used in pattern classification, including its Singular Value Decomposition (SVD) implementation, is primarily employed for dimensionality reduction and principal component extraction. This algorithm involves covariance matrix computation and eigenvalue decomposition techniques to identify the most significant features in high-dimensional data.
MATLAB implementation of PCA algorithm with comprehensive annotations and practical applications, featuring key functions like princomp for dimensionality reduction and data analysis techniques
Classical image fusion algorithms including IHS transformation, PCA-based fusion, and weighted fusion methods. The provided MATLAB/Python implementation is ready-to-run with comprehensive code documentation. Welcome to download and experiment with these practical fusion techniques.
MATLAB source code for PCA principal component analysis algorithm, implementing PCA using MATLAB. Includes related PCA materials, data preprocessing techniques, and result interpretation methods.
Face recognition code based on PCA algorithm, implementing eigenface extraction and face identification with mathematical transformation and comparison techniques
Implementation of PCA for image dimension reduction with feature extraction capabilities
PCA algorithm for feature extraction in facial recognition, which is the most widely used traditional facial recognition technique. This method achieves dimensionality reduction by constructing feature subspaces through eigenvalue decomposition of covariance matrices.
This MATLAB-based algorithm implements face detection and recognition using PCA (Principal Component Analysis) for the recognition component, featuring a GUI interface - simply execute facedec.m to run the complete system
Design of Face Recognition GUI using Principal Component Analysis (PCA) Algorithm for Image Processing and Pattern Recognition This project implements a comprehensive face recognition system through MATLAB's Graphical User Interface (GUI), utilizing Principal Component Analysis (PCA) for dimensionality reduction and feature extraction from facial images. The system involves training on sample datasets and testing recognition accuracy with robust image processing techniques.
A self-developed mini-experiment implementing face recognition algorithms in MATLAB using classical PCA methodology. The core implementation employs SVD-based feature extraction for PCA principal components in pixel space, with classification relying on minimum distance classification algorithm.