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Nonparametric Bayesian sparse factor models with application to gene expression modeling

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.

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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
Uncontrolled Keywords: stat.AP stat.AP cs.AI stat.ML
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:25
Last Modified: 20 Oct 2014 01:09
DOI: 10.1214/10-AOAS435

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