Probabilistic Component Latent Analysis (PLCA) is a statistical modeling
method for feature extraction from non-negative data. It has been fruitfully
applied to various research fields of information retrieval. However, the
EM-solved optimization problem coming with the parameter estimation of
PLCA-based models has never been properly posed and justified. We then propose
in this short paper to re-define the theoretical framework of this problem,
with the motivation of making it clearer to understand, and more admissible for
further developments of PLCA-based computational systems.