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.

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LDA: A Fundamental Algorithm in Face Recognition Systems

LDA serves as a foundational algorithm in face recognition, widely adopted for its effective feature extraction and dimensionality reduction capabilities in practical applications.

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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.

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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.

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MATLAB Source Code for Feature Extraction in Face Recognition

MATLAB source code for feature extraction in face recognition, implementing Linear Discriminant Analysis (LDA) methodology for dimensionality reduction and pattern classification.

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MATLAB Implementation Divided into Two Parts: PCA with Rough Sets and Fuzzy Neural Networks

This program consists of two main implementations: Part 1 combines PCA with Rough Sets and Fuzzy Neural Networks for face recognition, while Part 2 integrates PCA, LDA, Rough Sets and Fuzzy Neural Networks for pattern recognition. The implementation includes ORL face database handling, experimental results, and demonstrates practical approaches for dimensionality reduction and classification algorithms.

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Face Recognition using PCA, LDA, and BP Neural Network with GUI Implementation

A comprehensive face recognition system integrating Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Backpropagation Neural Network, featuring a functional GUI interface successfully implemented in MATLAB 8.0. Ideal for academic projects and graduation theses with complete code structure and modular implementation.

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LDA-Based Face Recognition Technology

Implementation of face recognition using Linear Discriminant Analysis (LDA), including data loading from the ORL database, dataset partitioning for training and testing phases, and evaluation of classification performance metrics.

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Comprehensive Face Recognition Code Collection

This collection contains various face recognition implementations including wavelet analysis, PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), and eigenface methods with detailed algorithm explanations and code descriptions.

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Integration of PCA and LDA Algorithms with Code Implementation for Face Recognition

This project combines Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for image training and recognition processes. Implemented using the ORL face database, the system achieves high recognition accuracy through optimized feature extraction and classification techniques. The implementation includes complete pipelines for data preprocessing, dimensionality reduction, and pattern matching.

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