Mixture of Common Factor AnalysersΒΆ
A mixture of common factor analysers (MCFA) is a method for modelling multi-dimensional data. The model assumes that the data are generated by a set of mutually orthogonal latent factors that are common to all data, but the scoring (or extent) of those factors is different for each data point. It also assumes that the scoring in latent space can be modelled as a mixture of multivariate Gaussian distributions. The latent space is assumed to be lower dimensional than the data.
Model parameters are estimated using the expectation-maximization algorithm, given some fixed number of latent factors and components. If the number of latent factors and components is not known then these are found through a grid search, where the minimum message length is adopted as the objective function.
Using Mock Getting started guide.