Expression Recognition Source Code with Direct PCA and SVM Function Calls
A facial expression recognition source code that directly implements PCA and SVM functions for efficient emotion classification.
Basic Mathematics Package Implementation - PCA
Implementing PCA using MATLAB's basic mathematics package helps understand PCA principles and demonstrates practical implementation steps including data preprocessing and eigendecomposition.
PCA+LDA Classical Face Recognition Algorithm
Classic face recognition algorithm using PCA for dimensionality reduction followed by LDA for feature extraction and classification enhancement
Comprehensive Dimensionality Reduction Algorithms Toolbox
This toolbox encompasses a diverse collection of dimensionality reduction algorithms, featuring traditional methods like PCA and Local PCA alongside classical manifold learning techniques such as Isomap, LLE, HLLE, Laplacian Eigenmaps, and Local Tangent Space Alignment. Each algorithm includes implementation insights and parameter configuration guidance for practical applications.
Pattern Recognition Assignment - Fully Custom Simulation
Pattern Recognition Assignment - Fully Custom Simulation Program. The implementation first applies Principal Component Analysis (PCA) for dimensionality reduction on the IRIS dataset, then classifies the reduced-dimensional data using the minimum error method. The compressed package includes MATLAB source code with detailed comments, a self-written report, and the IRIS dataset in .MAT format for program invocation. The program outputs final results to a text file with clear algorithmic implementation explanations.
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
Manifold Learning Algorithms Including MDS, PCA, ISOMAP, and LLE
Comprehensive Collection of Manifold Learning Algorithms Featuring MDS, PCA, ISOMAP, and LLE with Implementation Insights
MATLAB Code Implementation for Face Recognition Using PCA Algorithm
MATLAB source code for face recognition system implementing Principal Component Analysis (PCA) with feature extraction and pattern matching capabilities.
Window-Based Image Fusion Tool
A comprehensive window-based tool for image fusion containing almost all commonly used fusion algorithms: PCA (Principal Component Analysis), IHS transformation, pyramid algorithms, wavelet transformation, à-trous wavelet transform, and Brovey fusion with code-level implementation insights.
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.