PPCA is a probabilistic latent variable model, whose maximum likelihood solution corresponds to PCA. For an introduction to PPCA, see [1].
This implementation uses the expectation-maximization (EM) algorithm to find maximum-likelihood estimates of the PPCA model parameters. This enables a principled handling of missing values in the dataset. In this implementation, we use p(X_obs, Z) as the complete-data likelihood, where X_obs denotes the non-missing entries in the data X, and Z denotes the latent variables (see [4]).
This implementation was tested for correctness against the MATLAB ppca function.
This implementation uses numba (in addition to numpy) to accelerate the EM algorithm in the presence of missing values. It was tested with Python 3.12 and can be installed as a package with:
pip install -e .
To run the demo.ipynb notebook, the following packages are additionally required:
pip install notebook
pip install scikit-learn
pip install matplotlib
In the demo.ipynb we show basic usage and compare this implementation to the sklearn implementation. In short, PPCA can be used exactly like its sklearn counterpart:
from em_ppca import EMPPCA
...
em_ppca = EMPPCA(n_components=3)
# X contains data with possibly missing values (= np.nan)
# Z are the transformed values
Z = em_ppca.fit_transform(X)
print("principle components: ", em_ppca.components_)
...
The current implementation supports all attributes, but only the fit and fit_transform methods compared to the sklearn counterpart. Feedback / contributions are welcome!
[1] Bishop, C. M., Pattern Recognition and Machine Learning. New York: Springer, 2006.
[2] Tipping, M. E. and Bishop, C. M., Probabilistic Principal Component Analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 1999.
[3] Sam Roweis. EM algorithms for PCA and SPCA. In Proceedings of the 1997 conference on Advances in neural information processing systems 10 (NIPS '97), 1998, 626-632.
[4] Alexander Ilin and Tapani Raiko. 2010. Practical Approaches to Principal Component Analysis in the Presence of Missing Values. J. Mach. Learn. Res. 11 (August 2010), 1957-2000.