Explore MATLAB source code curated for "PCA" with clean implementations, documentation, and examples.
Comparative analysis of four image fusion techniques (IHS, PCA, Wavelet, and Wavelet-PCA Hybrid) between panchromatic and multispectral images, including implementation methods and evaluation metrics calculation (Information Entropy, Q4, etc.)
A facial recognition program implementing Principal Component Analysis (PCA) for feature extraction and Support Vector Machine (SVM) for classification - shared with the technical community!
Statistical pattern recognition methods in pattern recognition, including classical approaches like Bayesian methods, statistical learning, LDA, PCA, and SVM, provide an invaluable algorithmic toolbox with code implementation insights.
An effective and highly interpretable image classification approach utilizing Principal Component Analysis (PCA) for dimensionality reduction and feature extraction, with straightforward implementation.
Feature dimensionality reduction techniques including Principal Component Analysis (PCA) and feature selection methods such as SFFS (Sequential Floating Forward Selection), SBS (Sequential Backward Selection), and SFS (Sequential Forward Selection). These four commonly used dimensionality reduction approaches help optimize datasets by reducing feature dimensions while preserving critical information.
PCA+LDA Face Recognition achieves higher accuracy than standalone PCA or LDA algorithms, requiring MATLAB's Dimensionality Reduction Toolbox for feature preprocessing and implementation.
A PCA-based face recognition system with 40 sets of facial images. Includes random image selection and recognition demonstration with feature extraction and similarity measurement implementations.
MATLAB implementation for face image dimensionality reduction with PCA followed by neural network classifier training
Source code for PCA-based feature extraction implemented in MATLAB language (PCA refers to Principal Component Analysis)
Normalization of raw data followed by PCA dimensionality reduction using principal components as features for fuzzy clustering analysis via the fuzzy c-means algorithm