Nonparametric Bayesian Sparse Hierarchical Factor Modeling and Regression
Piyush Rai ⋅ Hal Daumé III
2008 Poster
Abstract
We propose a nonparametric Bayesian sparse factor analysis 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 in gene-expression analysis.
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