Kernel Principal Component Analysis (PCA): Explained with an Example
Dimensionality discount methods like PCA work splendidly when datasets are linearly separable—however they break down the second nonlinear patterns seem. That’s precisely what occurs with datasets equivalent to two moons: PCA flattens the construction and mixes the lessons collectively. Kernel PCA fixes this limitation by mapping the information right into a higher-dimensional function house the…
