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ICA
2007
Springer

Infinite Sparse Factor Analysis and Infinite Independent Components Analysis

11 years 3 months ago
Infinite Sparse Factor Analysis and Infinite Independent Components Analysis
Abstract. A nonparametric Bayesian extension of Independent Components Analysis (ICA) is proposed where observed data Y is modelled as a linear superposition, G, of a potentially infinite number of hidden sources, X. Whether a given source is active for a specific data point is specified by an infinite binary matrix, Z. The resulting sparse representation allows increased data reduction compared to standard ICA. We define a prior on Z using the Indian Buffet Process (IBP). We describe four variants of the model, with Gaussian or Laplacian priors on X and the one or two-parameter IBPs. We demonstrate Bayesian inference under these models using a Markov Chain Monte Carlo (MCMC) algorithm on synthetic and gene expression data and compare to standard ICA algorithms.
David Knowles, Zoubin Ghahramani
Added 16 Aug 2010
Updated 16 Aug 2010
Type Conference
Year 2007
Where ICA
Authors David Knowles, Zoubin Ghahramani
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