Hi Shishir,
please note that the ProbabilisiticPCA got recently refactored which lead to
API changes and improved documentation + examples.
https://github.com/scikit-learn/scikit-learn/pull/2404
Did you take these changes into account? If not it would be great to know which
exmample / reduplicate documentation you witnessed.
You can think of it as an additional PCA option to obtain a probabilistic score
when predicting the model fit on unseen data and to estimate the data
covariance.
The PCA components themselves will be the same in both variants. In other words
you can just apply it as you would regular PCA + additional arguments +
extended application.
HTH,
Denis
Also see:
Tipping, Michael E., and Christopher M. Bishop. "Probabilistic principal
component analysis." Journal of the Royal Statistical Society: Series B
(Statistical Methodology) 61.3 (1999): 611-622.
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