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.

## 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 |
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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: | 04 Feb 2015 22:35 |

Last Modified: | 05 May 2015 11:07 |

DOI: | 10.1214/10-AOAS435 |