This is a programme for realizing PPCA (Probabilistic Principal Component Analysis).
In this project, PPCA are realized through both python and R. Meanwhile, there are .Rmd and .ipynb files for convenient reading.
In addition, some examples including mixture PPCA and PPCA with missing data are also realized. These can be found in PPCA.Rmd.
For more details, please refer to the papers below:
[1] M. E. Tipping and C. M. Bishop, “Probabilistic Principal Component Analysis”, in Journal of the Royal Statistical Society, Series B (Statistical Methodology), Vol. 61, No. 3(1999) , pp. 611-622, doi:10.1111/1467-9868.00196.
[2] Ilin. A and Raiko. T, “Practical approaches to principal component analysis in the presence of missing values”, in Journal of Machine Learning Research, Vol. 11(2010), pp. 1957-2000, doi:10.5555/1756006.1859917.
[3] M. E. Tipping and C. M. Bishop, “Mixtures of Probabilistic Principal Component Analyzers”, in Neural Computation, Vol. 11, No. 2(1999), pp. 443-482, doi:10.1162/089976699300016728.