Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Proceedings of the 25th international conference on Machine learning
Infinite sparse factor analysis and infinite independent components analysis
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
IEEE Transactions on Signal Processing
Closed-Form EM for sparse coding and its application to source separation
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Dictionary Learning for Noisy and Incomplete Hyperspectral Images
SIAM Journal on Imaging Sciences
Stochastic variational inference
The Journal of Machine Learning Research
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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.