Explore MATLAB source code curated for "主成分分析" with clean implementations, documentation, and examples.
A practical guide to implementing PCA and CCA with real-world case studies, featuring code implementation approaches and result interpretation techniques.
Probabilistic Principal Component Analysis (PPCA) extends traditional PCA into a probabilistic framework, offering significant advantages. This introduction covers the derivation of PPCA and its relationship with standard PCA. In PPCA, observed data x is assumed to be generated by latent variables z, with both z and conditional probability p(x|z) following Gaussian distributions. Parameters are estimated using the Expectation-Maximization algorithm.
Implementation of a facial recognition system leveraging Principal Component Analysis (PCA) methodology
MATLAB Code Implementation for Image Processing Toolbox with Enhanced Technical Descriptions
MATLAB code implementation of Principal Component Analysis (PCA) function with detailed algorithmic explanations
Principal Component Analysis Effectively Implements Image Fusion with Optimized Spatial Feature Retention
PCA face recognition and its theoretical foundation with algorithm implementation insights
Image Denoising Through RBF Neural Network Learning with Supervised PCA Projection
Principal Component Analysis (PCA) - A Comprehensive Guide to Dimensionality Reduction Techniques with Practical Implementation Insights
MATLAB Implementation of Image Fusion Algorithms