Principal Component Analysis for Image Recognition and Feature Extraction
MATLAB-based PCA implementation for image recognition and feature extraction applications, featuring dimensionality reduction and pattern discovery capabilities.
MATLAB Code Implementation of Principal Component Analysis
MATLAB implementation of Principal Component Analysis, a commonly used tool in various image processing techniques with dimension reduction and noise removal capabilities
Face Recognition System Using Principal Component Analysis (PCA)
A face recognition system implementation based on Principal Component Analysis (PCA) with algorithm explanations and feature extraction techniques.
Implementation of Face Recognition System Using Principal Component Analysis (PCA)
This system employs 2D PCA algorithm to compute dimension reduction matrix for training set vectors, and utilizes nearest neighbor method to evaluate recognition accuracy on test datasets with code implementation insights.
MATLAB Implementation of Principal Component Analysis
Principal Component Analysis algorithm for image feature extraction and data dimensionality reduction applications
MATLAB Code Implementation for Image Processing Toolbox
MATLAB Image Processing Toolbox implementing Principal Component Analysis (PCA), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) methodologies.
Partial Least Squares Method
The Partial Least Squares (PLS) method refers to performing principal component analysis for dimensionality reduction on datasets before conducting linear regression analysis based on least squares. The following source code is provided in its complete form by the GreenSim team for free use, with proper attribution required to GreenSim team (http://blog.sina.com.cn/greensim). The implementation includes key components for covariance maximization and projection calculations.
Neural Network Algorithm with Principal Component Analysis
Neural network algorithm integrated with principal component analysis, with practical code implementation examples for enhanced utility
Powerful Statistical Pattern Recognition Toolbox
A comprehensive statistical pattern recognition toolbox featuring Gaussian classifier, Gaussian mixture models (GMM), principal component analysis (PCA), support vector machines (SVM), and other common classification algorithms with detailed implementation guidance.
PCA (Principal Component Analysis) Algorithm
The PCA (Principal Component Analysis) algorithm is widely applied in engineering and scientific research. This report investigates its fundamental structure and working principles. Conventional PCA primarily employs linear algorithms, but research reveals limitations such as inability to separate independent signal components from linear combinations, with principal components determined solely by second-order statistics (autocorrelation matrices) that only describe stationary Gaussian distributions. Improved versions include nonlinear PCA and robust algorithms. We demonstrate engineering applications through a line/plane fitting example using minor components (variance-minimizing elements) from component analysis.