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Nonparametric Factor Analysis with Beta Process Priors
This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. We propose a nonparametric extension to the factor analysis problem using a beta process prior. This beta process factor analysis (BPFA) 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.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
