Kernel PCA extends traditional PCA by enabling nonlinear dimensionality reduction using the kernel trick. It projects data into a higher-dimensional space, making complex patterns more separable and preserving structure during reduction.

Principal Component Analysis (PCA) is a powerful technique to reduce dimensionality while preserving essential data variance. It helps tackle the curse of dimensionality, simplifies complex datasets, and enhances model performance by extracting key features. This post breaks down PCA step-by-step, from geometric intuition and variance maximization to real-world applications and limitations.