MATLAB Toolkit for Common Feature Extraction Methods

This document introduces a MATLAB toolkit that incorporates commonly used feature extraction methodologies. The toolkit provides complete source code implementations for algorithms such as Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA), Maximum Noise Fraction (MNF), Partial Least Squares (PLS), along with their kernel-based variants: Kernel PCA (KPCA), Kernel CCA (KCCA), Kernel MNF (KMNF), and Kernel PLS (KPLS). Each implementation includes core functions for data preprocessing, covariance matrix computation, eigenvalue decomposition, and projection matrix calculation. These algorithms demonstrate versatility in processing diverse data types including images, audio signals, and text data. They find extensive applications in signal processing, pattern recognition, data mining, and computer vision domains. The toolkit's modular design allows researchers and engineers to easily integrate these methods into their workflows, featuring parameter configuration interfaces and result visualization capabilities. Particularly noteworthy are the kernel method implementations, which employ radial basis function (RBF) kernels and matrix operations to handle nonlinear data relationships. The code includes optimization for large datasets through efficient memory management and parallel computing options. This toolkit serves as a valuable resource for professionals seeking to enhance data analysis pipelines and develop practical solutions for real-world problems.