Probabilistic Principal Component Analysis

Probabilistic Principal Component Analysis (PPCA) extends traditional PCA into a probabilistic framework, offering significant advantages. This introduction covers the derivation of PPCA and its relationship with standard PCA. In PPCA, observed data x is assumed to be generated by latent variables z, with both z and conditional probability p(x|z) following Gaussian distributions. Parameters are estimated using the Expectation-Maximization algorithm.