ppca

Probabilistic principal component analysis. PPCA is a simplified factor analysis that employs a latent variable model with linear relationship:

    y ∼ W * x + μ + ε

where latent variables x ∼ N(0, I), error (or noise) ε ∼ N(0, Ψ), and μ is the location term (mean). In PPCA, an isotropic noise model is used, i.e., noise variances constrained to be equal (Ψ_i = σ²). A close form of estimation of above parameters can be obtained by maximum likelihood method.

====References:====

• Michael E. Tipping and Christopher M. Bishop. Probabilistic Principal Component Analysis. Journal of the Royal Statistical Society. Series B (Statistical Methodology) 61(3):611-622, 1999.

Parameters