Explore MATLAB source code curated for "降维" with clean implementations, documentation, and examples.
Self-developed PCA function for hyperspectral image processing using MATLAB implementation, primarily designed for dimensionality reduction with customizable code structure for various analytical requirements.
KPCA is primarily applied in image denoising, along with capabilities for feature extraction and dimensionality reduction, including code-level implementation approaches and kernel function selection strategies.
Comprehensive MATLAB programs for hyperspectral image processing featuring image fusion, dimensionality reduction, and maximum likelihood classification algorithms with practical implementation examples
ICA achieves dimensionality reduction and isolates independent neural activations in brain imaging studies. This demonstration compares icaMS, icaML, icaMF, icaMF (positive sources), and PCA algorithms using two fMRI datasets: human scans from Hvidovre University Hospital (Denmark) and monkey scans from the EU-funded MAPAWAMO project.
Face recognition based on 2DPCA provides superior image dimensionality reduction while achieving higher recognition accuracy compared to traditional PCA methods. This implementation preserves critical image features through direct matrix-to-matrix projection without vectorization.
MATLAB implementation of PCA for image feature extraction and dimensionality reduction with comprehensive code annotations
Implementation of Marginal Fisher Analysis algorithm for dimensionality reduction, complete with usage instructions and code annotations for learning and knowledge sharing purposes.
MATLAB code implementation of Principal Component Analysis (PCA) function with detailed algorithmic explanations
A Practical Implementation of Independent Component Analysis for Face Recognition Systems
PCA face recognition and its theoretical foundation with algorithm implementation insights