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A nonparametric Bayesian extension of Factor Analysis (FA) is proposed whereobserved data $\mathbf{Y}$ is modeled as a linear superposition, $\mathbf{G}$,of a potentially infinite number of hidden factors, $\mathbf{X}$. The IndianBuffet Process (IBP) is used as a prior on $\mathbf{G}$ to incorporate sparsityand to allow the number of latent features to be inferred. The model's utilityfor modeling gene expression data is investigated using randomly generated datasets based on a known sparse connectivity matrix for E. Coli, and on threebiological data sets of increasing complexity.

Nonparametric Bayesian sparse factor models with application to gene expression modeling