Hierarchical IFA Belief Networks
Abstract
We introduce a new real-valued belief network, which is a multilayer generalization of independent factor analysis (IFA). At each level, this network extracts real-valued latent variables that are non-linear functions of the input data with a highly adaptive functional form, resulting in a hierarchical distributed representation of these data. The network is based on a probabilistic generative model, constructed by cascading single-layer IFA models. Whereas exact maximum-likelihood learning for this model is intractable, we present and demonstrate an algorithm that maximizes a lower bound on the likelihood. This algorithm is developed by formulating a variational approach to hierarchical IFA networks.
Cite
Text
Attias. "Hierarchical IFA Belief Networks." Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999.Markdown
[Attias. "Hierarchical IFA Belief Networks." Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999.](https://mlanthology.org/aistats/1999/attias1999aistats-hierarchical/)BibTeX
@inproceedings{attias1999aistats-hierarchical,
title = {{Hierarchical IFA Belief Networks}},
author = {Attias, Hagai},
booktitle = {Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics},
year = {1999},
pages = {1-9},
volume = {R2},
url = {https://mlanthology.org/aistats/1999/attias1999aistats-hierarchical/}
}