Deep learning with hierarchical convolutional factor analysis.
Journal Article (Journal Article)
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.
Full Text
Duke Authors
Cited Authors
- Chen, B; Polatkan, G; Sapiro, G; Blei, D; Dunson, D; Carin, L
Published Date
- August 2013
Published In
Volume / Issue
- 35 / 8
Start / End Page
- 1887 - 1901
PubMed ID
- 23787342
Pubmed Central ID
- PMC3683114
Electronic International Standard Serial Number (EISSN)
- 1939-3539
International Standard Serial Number (ISSN)
- 0162-8828
Digital Object Identifier (DOI)
- 10.1109/tpami.2013.19
Language
- eng