Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey

Understand

This is a tutorial and survey paper on factor analysis, probabilistic Principal Component Analysis (PCA), variational inference, and Variational Autoencoder (VAE).

• These methods, which are tightly related, are dimensionality reduction and generative models.
• They assume that every data point is generated from or caused by a low-dimensional latent factor.
• By learning the parameters of distribution of latent space, the corresponding low-dimensional factors are found for the sake of dimensionality reduction.