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Deep Learning with Hierarchical Convolutional Factor Analysis.

Publication ,  Journal Article
Chen, B; Polatkan, G; Sapiro, G; Blei, D; Dunson, D; Carin, L
Published in: IEEE transactions on pattern analysis and machine intelligence
January 2013

Unsupervised multi-layered ("deep") models are considered for general data, with a particular focus on 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. In order to address large-scale and streaming data, an online version of VB is also developed. The number of basis functions or 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.

Duke Scholars

Published In

IEEE transactions on pattern analysis and machine intelligence

EISSN

1939-3539

ISSN

0162-8828

Publication Date

January 2013

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 0906 Electrical and Electronic Engineering
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing
 

Citation

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Chen, B., Polatkan, G., Sapiro, G., Blei, D., Dunson, D., & Carin, L. (2013). Deep Learning with Hierarchical Convolutional Factor Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Chen, B., G. Polatkan, G. Sapiro, D. Blei, D. Dunson, and L. Carin. “Deep Learning with Hierarchical Convolutional Factor Analysis.IEEE Transactions on Pattern Analysis and Machine Intelligence, January 2013.
Chen B, Polatkan G, Sapiro G, Blei D, Dunson D, Carin L. Deep Learning with Hierarchical Convolutional Factor Analysis. IEEE transactions on pattern analysis and machine intelligence. 2013 Jan;
Chen, B., et al. “Deep Learning with Hierarchical Convolutional Factor Analysis.IEEE Transactions on Pattern Analysis and Machine Intelligence, Jan. 2013.
Chen B, Polatkan G, Sapiro G, Blei D, Dunson D, Carin L. Deep Learning with Hierarchical Convolutional Factor Analysis. IEEE transactions on pattern analysis and machine intelligence. 2013 Jan;

Published In

IEEE transactions on pattern analysis and machine intelligence

EISSN

1939-3539

ISSN

0162-8828

Publication Date

January 2013

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 0906 Electrical and Electronic Engineering
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing