The Infinite Hierarchical Factor Regression Model
Machine Learning
2009-08-06 v1 Machine Learning
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
@article{arxiv.0908.0570,
title = {The Infinite Hierarchical Factor Regression Model},
author = {Piyush Rai and Hal Daumé},
journal= {arXiv preprint arXiv:0908.0570},
year = {2009}
}