MATLAB Code Implementation for Principal Component Analysis (PCA)
This code implements Principal Component Analysis (PCA) for feature selection, extracting the top three principal components with the highest variance contribution
Feature Extraction Using Principal Component Analysis
MATLAB source code for feature extraction through Principal Component Analysis in pattern recognition, including algorithm implementation and key function explanations
PCA Code for Principal Component Analysis with Implementation Guide
This Principal Component Analysis (PCA) code is designed for beginners learning face recognition, featuring comprehensive comments and step-by-step implementation details for dimensionality reduction and feature extraction.
Principal Component Analysis (PCA) Algorithm
Principal Component Analysis (PCA) Algorithm is a dimensionality reduction technique used to simplify complex datasets. This algorithm can be implemented using covariance matrix computation and eigenvalue decomposition to identify dominant patterns in data.
Discrete Wavelet Transform Followed by Principal Component Analysis for Pattern Recognition Applications
This approach employs Discrete Wavelet Transform (DWT) for feature extraction and Principal Component Analysis (PCA) for dimensionality reduction, applicable to pattern recognition systems including facial recognition, palmprint analysis, emotion detection, and fingerprint identification.
PCA-Based Feature Extraction Source Code
Source code for PCA-based feature extraction implemented in MATLAB language (PCA refers to Principal Component Analysis)
Principal Component Analysis with Scatter Plot Visualization of Top Three Principal Components
M-code implementation for principal component analysis with scatter plot visualization of the first three principal components, seeking guidance and feedback.
Face Recognition with PCA and LDA Implementation Code
MATLAB implementation of face recognition using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) algorithms with complete source code and detailed technical explanations.
Program Source Code for Remote Sensing Image Classification
Source code implementations for remote sensing image classification including methodologies such as Principal Component Analysis (PCA) and Neural Networks, with algorithm-specific implementation approaches
Principal Component Analysis Using Singular Value Decomposition (SVD) Method
Implementation of Principal Component Analysis (PCA) for Loaded Data Using Singular Value Decomposition (SVD) - Complete Source Code