Explore MATLAB source code curated for "PCA" with clean implementations, documentation, and examples.
Utilizing Principal Component Analysis to detect copy-move forgery regions in digital images with effective detection performance. The implementation involves feature extraction, dimensionality reduction, and similarity matching algorithms.
MATLAB source codes for three widely-used image fusion algorithms: IHS transformation, PCA-based fusion, and weighted fusion methods, including implementation details and algorithm explanations.
MATLAB implementation of Principal Component Analysis (PCA) and its enhanced variant Multilinear PCA (MPCA) for face recognition applications, featuring complete source code with dimensionality reduction and feature extraction functionalities.
Feature selection by combining PCA and ICA: performing principal component analysis first, followed by independent component analysis on the resulting features
A MATLAB implementation of a modified Principal Component Analysis (PCA) algorithm for face recognition applications, featuring enhanced image preprocessing and parameter optimization for improved accuracy.
Image Compression and Reconstruction via Principal Component Analysis (PCA) in MATLAB with Code Implementation Details
Comparative study of PCA+SVM and PCA+AdaBoost approaches for face detection, including detailed program explanations and algorithm implementation details.
This repository contains implementation source code for Principal Component Analysis (PCA) and Kernel PCA (KPCA), developed for intelligent technology courses with detailed algorithmic explanations and MATLAB/Python implementation considerations.
PCA, DPCA, KPCA, and KSPCA are highly effective for fault identification, fault diagnosis, and fault classification applications, providing robust solutions for industrial anomaly detection.
Original PCA implementation code with proven effectiveness - try it to experience its utility. This resource aims to assist users in understanding and applying dimensionality reduction techniques through practical MATLAB examples.