Explore MATLAB source code curated for "PCA" with clean implementations, documentation, and examples.
This is a PCA-based face recognition program that utilizes Principal Component Analysis for facial feature extraction and classification. The implementation includes dimensionality reduction techniques and pattern matching algorithms for efficient face identification.
An efficient PCA program for data dimensionality reduction, noise removal, and feature extraction. The implementation includes covariance matrix computation and eigenvalue decomposition for optimal component selection.
MATLAB source code implementations for three image fusion algorithms: IHS, PCA, and weighted fusion method
A comprehensive PCA program for feature extraction and dimensionality reduction, implementing covariance matrix computation and eigenvalue decomposition algorithms. Available for download with complete source code.
Implementation of PCA+SVM for face recognition with detailed code annotations and practical application guidelines
This repository contains comprehensive MATLAB code for Principal Component Analysis (PCA), featuring principal component extraction, variance contribution rate calculation, contribution rate histogram plotting, and additional implementation insights.
This collection contains various face recognition implementations including wavelet analysis, PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), and eigenface methods with detailed algorithm explanations and code descriptions.
This project combines Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for image training and recognition processes. Implemented using the ORL face database, the system achieves high recognition accuracy through optimized feature extraction and classification techniques. The implementation includes complete pipelines for data preprocessing, dimensionality reduction, and pattern matching.
An international MATLAB toolbox featuring implementations of MLR (Multiple Linear Regression), PCA (Principal Component Analysis), PLS (Partial Least Squares), and KPCR (Kernel Principal Component Regression) algorithms with customizable parameters
Principal Component Analysis (PCA) and Partial Least Squares (PLS) are widely used analytical tools. This code demonstrates their relationship through the Nonlinear Iterative Partial Least Squares (NIPALS) algorithm implementation.