kernel PCA (under construction)
(55 minutes to learn)
This concept is still under construction.
This concept has the prerequisites:
- Derive kernel PCA as a generalization of PCA.
Core resources (read/watch one of the following)
→ 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)
→ Machine Learning: a Probabilistic Perspective
A very comprehensive graudate-level machine learning textbook.
Location: Section 14.4.4, "Kernel PCA," pages 493-496
- 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
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