Principal Component Analysis (PCA) Algorithm Implementation
Implementing the Principal Component Analysis (PCA) algorithm using MATLAB with detailed code implementation insights
MATLAB Implementation of Wavelet Transform-Based Principal Component Analysis for Face Recognition
A MATLAB program demonstrating the application of wavelet transform combined with principal component analysis in face recognition systems, featuring preprocessing, feature extraction, and classification implementation.
MATLAB Face Recognition Code Using Principal Component Analysis (PCA)
MATLAB-based face recognition implementation with Principal Component Analysis (PCA) as the core algorithm, featuring dimensionality reduction and feature extraction
MATLAB Code Implementation for Principal Component Analysis
Principal Component Analysis (PCA) implementation in MATLAB with applications in multispectral image processing. Includes algorithm explanation and key function descriptions for dimensionality reduction and feature extraction.
Face Recognition with 2D Principal Component Analysis and Support Vector Machine
A novel approach for solving facial recognition problems by integrating two-dimensional Principal Component Analysis (2DPCA) for efficient feature vector extraction with Support Vector Machine (SVM) as a robust classification discriminant method. Experimental implementation involves database validation with results demonstrating significant classification rate improvements through optimal feature dimensionality reduction and kernel-based pattern separation.
Multiple Linear Regression Model with Principal Component Analysis
Multiple Linear Regression Model combined with Principal Component Analysis principles, expertly implemented using MATLAB programming with comprehensive code demonstrations!
Facial Recognition Using Principal Component Analysis and Neural Networks
Complete MATLAB implementation files for facial recognition combining Principal Component Analysis and neural network techniques
Reading Hyperspectral Remote Sensing Images and Performing Principal Component Analysis
This MATLAB-based program implements hyperspectral remote sensing image reading and principal component analysis, sorting and displaying results in descending order of contribution rate, with enhanced image processing capabilities.
MATLAB Face Recognition Source Code Implementation with PCA and Nearest Distance Algorithms
Face recognition source code implementing Principal Component Analysis (PCA) and nearest neighbor distance algorithms for facial feature identification and authentication systems
Factor Analysis and Principal Component Analysis
Factor Analysis is a statistical technique for extracting common factors from variable groups, while Principal Component Analysis is a multivariate statistical method that reduces multiple variables to a few composite indicators. From a mathematical perspective, PCA serves as a dimensionality reduction technique using orthogonal transformations to convert correlated variables into linearly uncorrelated principal components.