MATLAB Implementation of Face Recognition System
Face recognition program developed using Principal Component Analysis (PCA) algorithm, providing a functional solution for biometric identification applications
KPCA Algorithm MATLAB Implementation with Kernel-Based Principal Component Analysis
MATLAB source code for KPCA (Kernel Principal Component Analysis) algorithm featuring kernel-based dimensionality reduction implementation
Collaborative Communication: Image Classification using PCA with Grayscale and Texture Features
MATLAB program for image classification based on Principal Component Analysis, grayscale/color features, and texture analysis using Gray-Level Co-occurrence Matrix (GLCM), complete with sample images and implementation details.
Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is a statistical analysis method that transforms multiple indicators into a few comprehensive indicators. This source code provides MATLAB implementation with detailed analytical annotations, including algorithm explanations and key function descriptions for data dimensionality reduction.
Principal Component Analysis and Partial Least Squares
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.
Principal Component Analysis (PCA) Algorithm Implementation
MATLAB program code for Principal Component Analysis (PCA) algorithm: data standardization, covariance matrix computation, eigenvalue and eigenvector calculation, variance contribution rate analysis with enhanced implementation details
PCA Principal Component Analysis Implementation in MATLAB
MATLAB implementation of PCA for image feature extraction and dimensionality reduction with comprehensive code annotations
MATLAB Program for Principal Component Analysis with Contribution Rate Output and 2D Scatter Plot Visualization
This MATLAB program implements Principal Component Analysis (PCA) with functionality to output component contribution rates and generate 2D scatter plots for data visualization in principal component space. The code efficiently performs data dimensionality reduction while preserving essential information through eigenvalue decomposition and covariance matrix computation.
Fuzzy Independent Component Analysis + Principal Component Analysis for Face Recognition
Fuzzy Independent Component Analysis combined with Principal Component Analysis for face recognition, utilizing Fuzzy Support Vector Machines for classification with implementation of feature extraction and pattern recognition algorithms.
MATLAB Code Implementation of PLS Algorithm Toolbox
PLS Algorithm Toolbox: Partial Least Squares Regression ≈ Multiple Linear Regression Analysis + Canonical Correlation Analysis + Principal Component Analysis