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According to the PPCA paper
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. one of the key advantages of PPCA over PCA is that it can naturally handle missing data. The method is in section 4.1 of the paper; though it's not a great decription. It would be cool to have.
The text was updated successfully, but these errors were encountered:
It's more about the estimation of Gaussian model properties from the missing data, see section 11.2 in "Statistical Analysis with Missing Data",Little and Rubin, 2020.
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According to the PPCA paper
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.
one of the key advantages of PPCA over PCA is that it can naturally handle missing data.
The method is in section 4.1 of the paper; though it's not a great decription.
It would be cool to have.
The text was updated successfully, but these errors were encountered: