Papers Published

1. Paisley, J; Carin, L, Nonparametric factor analysis with beta process priors, Proceedings of the 26th International Conference On Machine Learning, ICML 2009 (January, 2009), pp. 777-784 [doi] .
   (last updated on 2024/12/31)

   Abstract:
   We propose a nonparametric extension to the factor analysis problem using a beta process prior. This beta process factor analysis (BP-FA) model allows for a dataset to be decomposed into a linear combination of a sparse set of factors, providing information on the underlying structure of the observations. As with the Dirichlet process, the beta process is a fully Bayesian conjugate prior, which allows for analytical posterior calculation and straightforward inference. We derive a variational Bayes inference algorithm and demonstrate the model on the MNIST digits and HGDP-CEPH cell line panel datasets.