Feature Selection Combining PCA and ICA
Feature selection by combining PCA and ICA: performing principal component analysis first, followed by independent component analysis on the resulting features
Image Compression and Reconstruction Based on Principal Component Analysis
Contains images with code implementation. The code is straightforward, primarily utilizing principal component analysis with key functions for dimensionality reduction and data reconstruction.
Principal Component Analysis for Hyperspectral Images
A compact MATLAB program implementing Principal Component Analysis (PCA) for hyperspectral images, requiring input data in .mat format for efficient dimensionality reduction and feature extraction.
Application of Fuzzy Support Vector Machine and Principal Component Analysis in Face Recognition
Integration of Fuzzy Support Vector Machine and Principal Component Analysis for Enhanced Face Recognition Systems
MATLAB Implementation of Principal Component Analysis for Fault Detection in TE Process Data
Implementing Principal Component Analysis for Fault Detection of Fault Type 1 in TE Process Model Data Using MATLAB with Statistical Process Control Charts
Principal Component Analysis: A Statistical Approach for Dimensionality Reduction
Principal Component Analysis (PCA) is a statistical technique that transforms multiple variables into fewer composite indicators through dimensionality reduction, implemented mathematically via eigenvalue decomposition of covariance matrices.
Principal Component Analysis
Principal Component Analysis Algorithm
MATLAB Code Implementation for Principal Component Analysis
Principal Component Analysis code implementation - A dimensionality reduction tool featuring PCA algorithm and feature extraction methods
Principal Component Analysis Implementation with MATLAB GUI
A MATLAB GUI-based application for principal component analysis, featuring an intuitive interface design and high reference value for understanding PCA implementation approaches.
Principal Component Analysis and Fuzzy Support Vector Machine for Face Recognition
A comprehensive face recognition program implementing PCA and Fuzzy SVM algorithms, thoroughly tested and optimized for reliable performance