Deep learning with hierarchical convolutional factor analysis

Bo Chen, Gungor Polatkan, Guillermo Sapiro, David Blei, David Dunson, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

89 Scopus citations


Unsupervised multilayered (deep) models are considered for imagery. The model is represented using a hierarchical convolutional factor-analysis construction, with sparse factor loadings and scores. The computation of layer-dependent model parameters is implemented within a Bayesian setting, employing a Gibbs sampler and variational Bayesian (VB) analysis that explicitly exploit the convolutional nature of the expansion. To address large-scale and streaming data, an online version of VB is also developed. The number of dictionary elements at each layer is inferred from the data, based on a beta-Bernoulli implementation of the Indian buffet process. Example results are presented for several image-processing applications, with comparisons to related models in the literature. © 1979-2012 IEEE.
Original languageEnglish (US)
Pages (from-to)1887-1901
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number8
StatePublished - Jul 12 2013
Externally publishedYes

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