Data Dimensionality Reduction Toolbox
Data Dimensionality Reduction Toolbox featuring classical algorithms including PCA, LLE, MDS, LDA with implementation details and parameter customization
PCA+LDA Classical Face Recognition Algorithm
Classic face recognition algorithm using PCA for dimensionality reduction followed by LDA for feature extraction and classification enhancement
Face Recognition Using PCA and LDA
Face recognition implementation based on PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The main function loads image files, applies preprocessing techniques, executes the face recognition algorithm with dimensionality reduction, and generates performance accuracy plots.
Face Recognition Using PCA, LDA, and MMC Algorithms
MATLAB implementation of face recognition techniques employing PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), and MMC (Maximum Margin Criterion) algorithms with practical code examples
Statistical Pattern Recognition Methods in Pattern Recognition
Statistical pattern recognition methods in pattern recognition, including classical approaches like Bayesian methods, statistical learning, LDA, PCA, and SVM, provide an invaluable algorithmic toolbox with code implementation insights.
PCA+LDA Face Recognition with Enhanced Feature Dimensionality Reduction
PCA+LDA Face Recognition achieves higher accuracy than standalone PCA or LDA algorithms, requiring MATLAB's Dimensionality Reduction Toolbox for feature preprocessing and implementation.
LDA-Based Face Feature Extraction and Recognition with MATLAB Implementation
Implementation of LDA for face feature extraction and recognition using MATLAB, including code development and comprehensive result analysis with performance metrics
Linear Discriminant Analysis (LDA) for Ear Recognition: Methods Comparison and Implementation
Linear Discriminant Analysis (LDA) is a widely-used linear classification method for feature extraction, but its direct application to ear recognition faces dimensionality and small sample size problems. Researchers have developed multiple solutions to address these challenges, implementing various LDA-based ear recognition approaches. This article provides theoretical comparisons and experimental validation of four methods: Fisherears, DLDA, VDLDA, and VDFLDA, with implementation insights and performance analysis demonstrating VDFLDA's superiority.
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
Linear Discriminant Analysis (LDA) for Feature Selection
Linear Discriminant Analysis (LDA) for feature selection enables extraction of discriminative features from datasets or images, commonly applied in machine learning tasks such as classification or clustering. The method involves maximizing class separability through dimensionality reduction.