Project details for Nonparametric Sparse Factor Analysis

Logo Nonparametric Sparse Factor Analysis 1

by davidknowles - July 26, 2013, 01:02:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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This is the core MCMC sampler for the nonparametric sparse factor analysis model presented in

David A. Knowles and Zoubin Ghahramani (2011). Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling. Annals of Applied Statistics

From the abstract:

A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data Y is modeled as a linear superposition, G, of a potentially infinite number of hidden factors, X. The Indian Buffet Process (IBP) is used as a prior on 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 datasets based on a known sparse connectivity matrix for E. Coli, and on three biological datasets of increasing complexity.

Changes to previous version:

Initial Announcement on

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Agnostic
Data Formats: Agnostic
Tags: Nonparametric Bayes, Factor Analysis
Archive: download here


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