Kernel Principal Component Analysis (Kernel PCA) - Algorithm Implementation and Applications

Kernel Principal Component Analysis (Kernel PCA) extends traditional PCA using kernel methods to perform nonlinear dimensionality reduction through mapping to reproducing kernel Hilbert spaces, with Python implementation examples using scikit-learn.

Markov Random Field Denoising Examples with Kernel PCA

Image Denoising Using Markov Random Fields and Kernel Principal Component Analysis