What is kernel PCA?
Updated May 16, 2026
Short answer
Kernel PCA extends PCA to nonlinear feature spaces using kernel tricks.
Deep explanation
It applies kernel functions to implicitly map data into higher-dimensional space and then performs PCA in that space.
Real-world example
Used in nonlinear pattern recognition tasks.
Common mistakes
- Choosing inappropriate kernel functions.
Follow-up questions
- Why use kernel PCA?
- What are common kernels?