probabilistic PCA

(1.6 hours to learn)


Probabilistic principal component analysis (PCA) is a formulation of PCA as a latent variable model. Each data point is assumed to be generated as a linear function of Gaussian latent variables, plus noise. Like PCA, it has a closed form solution in terms of the truncated SVD of the covariance matrix.


This concept has the prerequisites:

Core resources (read/watch one of the following)


Supplemental resources (the following are optional, but you may find them useful)


Bayesian Reasoning and Machine Learning
A textbook for a graudate machine learning course.
Author: David Barber
Additional dependencies:
  • factor analysis


See also