MATLAB Simulation Program for Fault Diagnosis Using PCA Algorithm
MATLAB simulation program implementing Principal Component Analysis (PCA) for fault detection and diagnosis systems, featuring algorithm demonstration and performance optimization
MATLAB Implementation of PCA Algorithm for Dimensionality Reduction
Dimensionality reduction using PCA algorithm to extract principal eigenvalues and reduce data dimensions, with MATLAB code implementation details.
MATLAB Source Code Implementation of PCA Algorithm
PCA algorithm MATLAB source code collaboratively developed with classmates - this implementation includes data standardization, covariance matrix computation, eigenvalue decomposition, and dimensionality reduction features for educational reference and technical discussion.
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.
PCA Algorithm Implementation in MATLAB
MATLAB implementation of PCA algorithm for dimensionality reduction, suitable for various applications including facial recognition systems with detailed code explanation
PCA Algorithm in Face Recognition with MATLAB Implementation
MATLAB implementation of PCA algorithm for face recognition using nearest neighbor classifier for identification, featuring data preprocessing, feature extraction, and classification techniques.
Face Recognition Using PCA Algorithm
Implementation of PCA-Based Face Recognition with Feature Extraction and Pattern Matching