AI engineering glossary · Classical ML algorithms
What is Kernel PCA?
Kernel PCA: An extension of principal component analysis that uses the kernel trick so it can find curved, non-linear structure. It runs the analysis in an implicit high-dimensional space rather than on original coordinates.
Kernel PCA: an example
Unrolling two concentric rings so a linear classifier can separate them after projection.
Related terms
More Classical ML algorithms terms
- Kernel trick
- Laplace smoothing
- Lasso regression
- Lazy learning
- Lift
- LightGBM
- Linear discriminant analysis
- Linear regression
Where you learn it
Kernel PCA is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.