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What is Linear Factor Models in Deep Learning about?

Chapter 13 discusses linear factor models, which are probabilistic models that utilize latent variables to represent data and enable generative modeling. It covers specific types of linear factor models, including probabilistic PCA, factor analysis, and independent component analysis (ICA), highlighting their applications and differences in noise distribution and latent variable priors. Additionally, it introduces slow feature analysis (SFA), which aims to learn invariant features from time signals by lever

Author

Nghĩa Bùi

Language

EN