kernel PCA (under construction)
(55 minutes to learn)
Summary
-No Summary-
Notes
This concept is still under construction.
Context
This concept has the prerequisites:
- principal component analysis
- the kernel trick
- spectral decomposition (The spectral decomposition is used in deriving kernel PCA.)
Goals
- Derive kernel PCA as a generalization of PCA.
Core resources (read/watch one of the following)
-Paid-
→ Pattern Recognition and Machine Learning
A textbook for a graduate machine learning course, with a focus on Bayesian methods.
Location:
Section 12.3, "Kernel PCA," pages 586-590
Supplemental resources (the following are optional, but you may find them useful)
-Paid-
→ Machine Learning: a Probabilistic Perspective
A very comprehensive graudate-level machine learning textbook.
Location:
Section 14.4.4, "Kernel PCA," pages 493-496
See also
- Some other ways of learning nonlinear representations of data:
- feed-forward neural nets , which adapt the representations in the context of some other learning objective
- spectral embeddings , a very similar algorithm based on spectral graph theory