PCA-Based Bearing Fault Diagnosis Program
A PCA bearing fault diagnosis program complete with sample datasets and execution results demonstrating fault detection capabilities through dimensionality reduction and pattern recognition techniques.
PCA+LDA+Rough Set+Fuzzy Neural Network Implementation for ORL Face Image Processing
This package contains 5 MATLAB codes implementing a comprehensive face recognition pipeline: 1) saveORLimage.m divides ORL face database into test set (ptest) and training set (pstudy), saved as imagedata.mat; 2) savelda.m performs PCA dimensionality reduction followed by LDA feature extraction, generating new test set (ldatest) and training set (ldastudy) saved as imageldadata.mat; 3) discretimage.m discretizes ldastudy data into discrete matrix disdata, stored as imagedisdata.mat; 4) savers.m constructs decision tables from disdata
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
Machine Learning Course Assignment: PCA, LDA Dimensionality Reduction with Naive Bayes Classifier Comparison
A machine learning course assignment implementing PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) for dimensionality reduction. Unlike many online resources with sparse comments, this implementation includes comprehensive annotations and attention to implementation details. Features a comparative Naive Bayes classifier and uses the OLR face image dataset. Important: ReducedDim parameter specifies the exact number of features to extract, not a percentage.
MATLAB Gesture Detection and Recognition System
Application Background: This gesture recognition simulation program serves as an excellent learning resource for MATLAB beginners to understand programming concepts. Once familiar with the implementation, users can extend functionality for algorithm validation and practical applications. Key Technology: The program employs popular image recognition algorithms including PCA (Principal Component Analysis) and feature extraction methods to identify regions of interest within images and interpret their semantic meaning. The implementation utilizes feature matching techniques to enhance recognition accuracy and reliability.
PCA-Based Face Detection and Recognition Algorithm Implementation
Implementation of PCA-based face detection and recognition algorithms with code-level explanations and practical applications
Gabor+PCA Face Recognition with MATLAB Implementation
MATLAB code for face recognition using Gabor filters combined with PCA, achieving excellent recognition rates through advanced feature extraction and dimensionality reduction techniques.
Comprehensive Image Fusion Program Collection
Image fusion programs implementing various algorithms including IHS, PCA, Laplacian Pyramid, PCNN, and Wavelet Transform - includes test images, thoroughly debugged and guaranteed to run properly. This represents the carefully developed core work of my graduation thesis.
32 Dimensionality Reduction Programs
This MATLAB toolbox includes 32 dimensionality reduction programs, featuring over a dozen algorithm packages such as PCA, LDA, MDS, and more. Particularly valuable for image processing applications, the toolbox provides comprehensive implementations with configurable parameters and visualization capabilities for various data analysis tasks.
Face Feature Extraction Using LDA and PCA Pattern Recognition Methods
This MATLAB program framework implements face feature extraction using LDA (Linear Discriminant Analysis) and PCA (Principal Component Analysis) pattern recognition methods, offering excellent performance for facial recognition applications.