midPCA
What is Kernel PCA?
Updated May 17, 2026
Short answer
Kernel PCA extends PCA to nonlinear transformations using kernel methods.
Deep explanation
Kernel PCA applies kernel trick to map data into higher-dimensional space where linear PCA is performed. This captures nonlinear structures in data.
Real-world example
Nonlinear pattern recognition in image classification.
Common mistakes
- Assuming PCA itself is nonlinear.
Follow-up questions
- What kernels are used?
- Why use kernel PCA?