Fault Diagnosis and Identification for TE Model Using KPCA (Kernel Principal Component Analysis)
Fault diagnosis and identification for TE model based on KPCA (Kernel Principal Component Analysis) methodology
KPCA-Based Fault Diagnosis Program
A straightforward fault diagnosis program utilizing KPCA with comprehensive annotations for ease of use
MATLAB Implementation of KPCA (Kernel Principal Component Analysis)
A well-designed KPCA program with comprehensive code comments, featuring kernel function implementation and eigenvalue decomposition for effective dimensionality reduction.
MATLAB Implementation of Classic KPCA
Classic KPCA program written by the founder of KPCA, providing a learning template with comprehensive kernel function implementations and dimensionality reduction demonstrations.
KPCA: Kernel Principal Component Analysis for Dimensionality Reduction
KPCA implementation featuring automatic dimensionality selection based on variance contribution rate, with kernel method integration for nonlinear data transformation
MATLAB Implementation of KPCA with Fault Diagnosis Capabilities
Original KPCA algorithm implementation featuring T2 and SPE statistical monitoring charts for comprehensive fault diagnosis systems
MATLAB Toolkit for Common Feature Extraction Methods
A comprehensive MATLAB toolkit containing implementations of essential feature extraction algorithms including PCA, CCA, MNF, PLS, KPCA, KCCA, KMNF, and KPLS with complete source code and mathematical formulations.
Principal Component Analysis for Facial Feature Recognition
Implementation of PCA, Fisher Linear Discriminant, Kernel PCA (KPCA), and 2D Discrete Wavelet Transform (DWT2) for facial recognition systems with dimensionality reduction techniques
Complete PCA and KPCA Implementation Programs for Face Recognition
Complete implementation programs for PCA (Principal Component Analysis) and KPCA (Kernel Principal Component Analysis), highly valuable for face recognition research with practical code examples and feature extraction methodologies
Source Code for PCA and KPCA Algorithms
This repository contains implementation source code for Principal Component Analysis (PCA) and Kernel PCA (KPCA), developed for intelligent technology courses with detailed algorithmic explanations and MATLAB/Python implementation considerations.