This python package implements the Mixtures of Common Factor Analyzers model
introduced by Baek+ 2010. It
uses tensorflow to implement a stochastic
gradient descent, which allows for model training without prior imputation of
missing data. The interface resembles the sklearn model API.
Refer to the docs/documentation.ipynb for the documentation and
docs/4d_gaussian.ipynb for an example application.
Install from PyPi using pip:
$ pip install mcfa
The minimum required python version is 3.8.
- EMMIXmfa in
R - Casey+ 2019 in
python
Compared to this implementation, Casey+ 2019 use an EM-algorithm instead of a stochastic gradient descent. This requires the imputation of the missing values before the model training. On the other hand, there are more initialization routines the lower space loadings and factors available in the Casey+ 2019 implementation.