Nonparametric factor analysis with beta process priors

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Nonparametric factor analysis with beta process priors

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ACM International Conference Proceeding Series; Vol. 382 archive
Proceedings of the 26th Annual International Conference on Machine Learning table of contents

Montreal, Quebec, Canada

Pages 777-784

Year of Publication: 2009

ISBN:978-1-60558-516-1

Authors

John Paisley	Duke University, Durham, NC
Lawrence Carin	Duke University, Durham, NC

Sponsors

: MITACS
: NSF
Microsoft Research : Microsoft Research

Publisher

ACM New York, NY, USA

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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 varia-tional Bayes inference algorithm and demonstrate the model on the MNIST digits and HGDP-CEPH cell line panel datasets.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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