Knowles, D and Ghahramani, Z (2010) Nonparametric Bayesian sparse factor models with application to gene expression modeling. Annals of Applied Statistics, 5. pp. 1534-1552.
Full text not available from this repository.Abstract
A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data $\mathbf{Y}$ is modeled as a linear superposition, $\mathbf{G}$, of a potentially infinite number of hidden factors, $\mathbf{X}$. The Indian Buffet Process (IBP) is used as a prior on $\mathbf{G}$ to incorporate sparsity and to allow the number of latent features to be inferred. The model's utility for modeling gene expression data is investigated using randomly generated data sets based on a known sparse connectivity matrix for E. Coli, and on three biological data sets of increasing complexity.
| Item Type: | Article |
|---|---|
| Additional Information: | Published in at http://dx.doi.org/10.1214/10-AOAS435 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org) |
| Subjects: | UNSPECIFIED |
| Divisions: | Div F > Computational and Biological Learning |
| Depositing User: | Cron Job |
| Date Deposited: | 28 Oct 2011 17:07 |
| Last Modified: | 17 May 2013 19:07 |
| DOI: | 10.1214/10-AOAS435 |
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