Probabilistic PCA with Missing Values
Probabilistic PCA which is applicable also on data with missing values. Missing value estimation is typically better than NIPALS but also slower to compute and uses more memory. A port to Python of the implementation by Jakob Verbeek.
from pyppca import ppca C, ss, M, X, Ye = ppca(Y,d,dia)
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