MATLAB Implementation of Kernel Entropy Component Analysis (KECA) Code

This is a MATLAB-implemented code for Kernel Entropy Component Analysis (KECA), a nonlinear dimensionality reduction technique. The core concept involves using kernel functions to map high-dimensional data into lower-dimensional spaces. In KECA implementation, we calculate kernel entropy to measure data complexity, facilitating the construction of an optimal low-dimensional subspace. The code incorporates several commonly used kernel functions, including linear kernel, polynomial kernel, and Gaussian (RBF) kernel, with configurable parameters for each. Additionally, we have implemented visualization methods to better demonstrate dimensionality reduction effects through scatter plots and entropy distribution graphs. The implementation features key functions for kernel matrix computation, entropy evaluation, and eigenvalue decomposition, providing clear insights into the algorithm's workflow. For those interested in KECA methodology, studying this code will offer comprehensive understanding of both theoretical principles and practical implementation approaches, including data preprocessing steps and result interpretation techniques.