By formulating PCA as a Bayesian model, we can auotmatically choose a latent dimensionality by maximizing the (approximate) marginal likelihood of the model.
This concept has the prerequisites:
- Know the definition of the Bayesian PCA model
- How can it be used to select the dimension of the latent space?
- Know of a way to approximate the marginal likelihood (e.g. the evidence approximation)
Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)
Supplemental resources (the following are optional, but you may find them useful)
→ Pattern Recognition and Machine Learning
A textbook for a graduate machine learning course, with a focus on Bayesian methods.
Location: Section 12.2.3, pages 580-583
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