Understanding the Probabilistic Latent Component Analysis Framework
Methodology
2017-03-16 v1
Abstract
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.
Keywords
Cite
@article{arxiv.1703.05208,
title = {Understanding the Probabilistic Latent Component Analysis Framework},
author = {D. Cazau and G. Nuel},
journal= {arXiv preprint arXiv:1703.05208},
year = {2017}
}