The Infinite Hierarchical Factor Regression Model
Abstract
We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coalescent. We apply this model to two problems (factor analysis and factor regression) in gene-expression data analysis.
Cite
Text
Rai and Daume. "The Infinite Hierarchical Factor Regression Model." Neural Information Processing Systems, 2008.Markdown
[Rai and Daume. "The Infinite Hierarchical Factor Regression Model." Neural Information Processing Systems, 2008.](https://mlanthology.org/neurips/2008/rai2008neurips-infinite/)BibTeX
@inproceedings{rai2008neurips-infinite,
title = {{The Infinite Hierarchical Factor Regression Model}},
author = {Rai, Piyush and Daume, Hal},
booktitle = {Neural Information Processing Systems},
year = {2008},
pages = {1321-1328},
url = {https://mlanthology.org/neurips/2008/rai2008neurips-infinite/}
}